PRE2023 1 Group1: Difference between revisions

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==Problem statement==
==Abstract==
The problem that will be addressed in this project is the development of an algorithm that finds the moment the price for electricity is the lowest for electric devices, reducing the amount of money one loses to its yearly electricity bill.  With the variable fee of the energy contract, the timing of the use of your electric device matters for the price one pays for the electricity. At moments when the energy generation exceeds the demand of electricity, the price will be low. An smart algorithm putting on the dish washer or washing machine at the moment the price is the best during the night could be profitable. Today, people can already take advantage of the hourly fluctuating prices by watching the costs of a kilowatt in an app, and then setting the time of an high electricity demanding device at an inexpensive moment. This is a practice for some people, but not everyone likes put in the effort to do this consistently. For those this algortihm combined with an app that does the work for you can come in handy, in particular if the algoritm can be connected to smart electric devices to operate them, making sure the system operates on its own without the need of human activity.


==Project Requirements, Preferences, and Constraints==
This paper focusses on the research and the development of an app that increases the profit from a dynamic electricity contract and decreased the effort needed from the consumer with respect to the current dynamic electricity apps. From literature research and user experience with the app it was found that the app would help consumers use a dynamic electricity contract more efficiently and would help with the implementation of green energy. The state of the art is discussed, the deliverables and the users are listed. Then the design of the app, the algorithm behind the app and the challenges faced during the development are discussed. After this the result of the user study is given, followed by a conlclusion and discussion with future recommedations.
Creating RPC criteria


Setting requirements
==Introduction==
Since the emergence of more renewable energy and of more energy intensive technologies, like air conditioning needed due to rising temperatures and more extreme weather, the electricity landscape has changed. The increase in load on the electricity net has to be counteracted by either improvements of the network or measures to decrease the peak load on the net. Next to this consumers often pay high prices for electricity due to producers having to power on fossil fuel plants to reach the electricity production needed for the peak times of the day, while sometimes renewable electricity is lost due to lack of use during high sun low use times. To fix this problem producers and governments have turned to introducing dynamic electricity contracts, where consumers pay different amounts of money depending on the time of consumption, thus making it more financially wise to use electricity on the low peak times. This usage of the electricity on different times can even lead to profits on the end of the consumer, and helps the planet and producers to increase the share of renewable sources of electricity.       


Setting preferences
==='''Problem statement'''===
The dynamic electricity contract is now done manually by the consumer. The consumer takes a graph of the electricity prices per hour and based on that plans the use of their flexible electric devices. However, the consumer can only optimize this to a certain extent. An algorithm that knows the exact cycles of electric devices can calculate with more precision the most favourable time for the electric devices.


Setting constraints
<br />


===RPC-list===
An algorithm should be developed that has the data from all electric devices regarding the electricity use as a function of time for each cycle. As an illustration, the cycle dishwasher uses more electricity in the beginning to warm up the water then at the end of the cycle. The algorithm should then calculate the total price of the cycle based on the hourly electricity cost graph with a correlation function. The algorithm then needs as an input the timeframe the electric devices have to operate in. The resulting starting times should be displayed in an application. The application has to be easily accessible for users, this could be either done with a website or an app, the app will probably lead to the best possible integration since messages that have to be send would in case of the website require the phone number of the user. The app would then also be connected to the other systems the user has such as their electricity providers app, the app of smart appliances and app of the electric car. With all this the system, most likely in app form, would then become a hub for all the users electricity based needs. Where the system even optimizes dynamic electricity pricing to be as profitable as possible, in turn helping the electricity network by de-loading the network at peak times enabling the better inclusion of renewable forms of electricity. Another important factor to take into account for the system is that solar panel users need other advice than people without solar panels, since the solar panels can sometimes cost money, due to negative prices, or can save much money due to times of high solar intensity. The negative prices are caused by over congestion on the electricity grid, which if not handled would lead to failure of the grid, this is especially caused by the large amount of renewable, unstable sources of electricity. Even the minister of energy of the Netherlands states that it is sometimes more beneficial for consumers with solar panels to turn them off in times of high negative prices, this is something that the system could also control or at least mention to the consumer. In times of no negative prices the use of solar electricity is always free, thus the system must take this into account. The price of electricity might be lowest in the morning but when the solar intensity is higher in the afternoon it would be more cost beneficial to turn on the appliances during times that the consumers' solar panels are peaking in electricity generation.
 
 
The dynamic electricity contract needs, at the moment, still a large amount of effort and input from the consumer to achieve the promised savings on electricity bills. Which thus leads to less people switching over to a dynamic electricity contract hampering the decrease of load on the net and inclusion of renewable electricity sources.
 
A system is thus needed to decrease the effort needed to be done by the consumer, such a system would in the best case automate all appliances based on the before set operation times and the known electricity prices. This automation would need a house full of smart appliances, which are connected to the internet and can thus be turned on remotely. Research done into these systems has shown that consumers need direct messages in order to be reminded of the time to turn on the appliances, which then leads to a efficient use of the dynamic electricity contract, but this research has also shown that consumers would rather have everything automated. This automation however is still very hard due to the lack of smart appliances in most households. Research also shows that consumers benefit from an overview of their electricity usage. The system to solve this problem would thus need at minimum an overview of electricity usage, a time window input, to determine the times the consumer would accept and be able to turn on the appliances, a message that is send at the prime-time the consumer has to turn on the appliance which could include the savings achieved by the to be undertaken action and in best case a direct link to the smart appliances to automate the activation of the appliances. Another situation that is interesting for our product is consumers in possession of an electric car. They could use their electric cars as electricity storage, for this to work however many things have to be kept in mind. The electric car has to still be charged for the travel to be done by the user, thus another input is needed where the minimum charge can be set by the user, this in turn then enables the system to better optimize electricity use since the car could be charged at times of low prices and then discharged at peak times thus leading to even more savings for the user. The health of the car battery however has also to be taken into account, since the savings on electricity bills would need to counteract the degradation of the batteries well enough to be worth it. This is dependent on the age of the car as well as the type of car. The system would thus need much more info and should in the best case be directly connected to the software of the electric car.
 
==State-of-the-art==
To get a firm grasp of our subject we conducted literature research by reading numerous articles related to the subject. We documented our research below in the form of a short summary of each article respectively.
 
===Research on consumer risks and benefits of dynamic electricity price contracts<ref>Van Hummelen, S., & Frizis, I. (2022). ''Research on consumer risks and benefits of dynamic electricity price contracts – A risk or an opportunity to save?'' Cambridge econometrics (Belgium). <nowiki>https://www.conpolicy.de/en/news-detail/research-on-consumer-risks-and-benefits-of-dynamic-electricity-price-contracts-a-risk-or-an-opport</nowiki></ref>===
This research states that there is serious risk involved in switching from fixed prices to dynamic prices. It concludes that there is only little room for flexible electric consumption, and that the average dynamic prices in France and Austria were higher in 2021 than the fixed price. Furthermore it is said that for households with an electric vehicle(EV) a dynamic electricity bill could be beneficial, since a EV is the biggest consumer within the flexible electricity consumption activities. However, in this paper it is stated that most of the electric consumption is used for space heating and water heating. In the Netherlands we use natural gas to warm up our homes, so the situation might be different and more profitable for Dutch households.
 
===Asset Study on Dynamic retail electricity prices<ref>Boeve, S., Cherkasky, J., Bons, M., Schult, H., Nabe, C., & Kielichowska, I. (2021). ''ASSET study on dynamic retail electricity prices.'' Publications Office of the EU. <nowiki>https://op.europa.eu/en/publication-detail/-/publication/a8b8e55f-a17f-11eb-b85c-01aa75ed71a1/language-en</nowiki></ref>===
This research says that consumers can significantly decrease their electricity bill by shifting to low price moments. It evaluates different kinds of dynamic pricing options, Real time pricing, time of use up till critical peak pricing. Real time pricing and time of use pricing are the riskiest but yield the highest reward, the critical peak pricing has the lowest risk but yield a lower reward. This research also states that a dynamic pricing leads to a more efficient electric grid, since lower peak demand reduces the losses in the electricity grid. This also results in a lower electricity bill.  Additionally, dynamic prices incentivise demand shifting to times of lower prices which usually indicate times of high intermittent renewable energy resources (RES) feed-in. The use of excess electricity can reduce local congestion and therefore facilitate the integration of RES in the energy system. Therefore it would also be in the interest of the government to promote switching to dynamic prices.
 
Furthermore it gives an overview of the potential customers for dynamic pricing. With a premium on the electricity prices reducing the maximum prices, up to 90 percent of the costumers could profit from dynamic pricing.
 
===Dynamic electricity pricing — Which programs do consumers prefer?<ref>Dütschke, E., & Paetz, A. (2013). Dynamic electricity pricing—Which programs do consumers prefer? ''Energy Policy'', ''59'', 226–234. <nowiki>https://doi.org/10.1016/j.enpol.2013.03.025</nowiki></ref>===
TOU vs RTP, RTP is real time pricing where the user pays based on the real time market prices which change every hour. TOU is time of use pricing where the price is fixed long in advance on a timetable.  
 
Consumers are fine with using dynamic electricity pricing as long as their daily routine is not affected by it or lead to reductions in their comfort level. When asked about electricity contracts people seem to still prefer a static contract. People tested in a dynamic pricing situation proposed multiple insights. Things like lights, tv, stove were things where the price at that point was not really taken into consideration, since the people thought it would affect their lifestyle too much. Things like dishwashers, washing machines and tumble dryers were used much more at low price times. Due to work however the participants of the test could not always benefit from the low prices and seemed to be less willing to turn on the appliances very early or very late in the day. People also preferred RTP over TOU since RTP made the users feel that they could save more money.  
 
Via a questionnaire it was found out that most people prefer a constant rate even though the advantages of a dynamic contract were explained thoroughly. The cost-saving expectation was 50 – 150 euro but turned out to only be 20 to 60 euro. Therefore, it is necessary that the other advantage of dynamic pricing, the load shifting, to be explained thoroughly. And the participants expressed a wish for demand automation, thus turning on the appliances at low price times automatically in order to make the dynamic pricing seem worthwhile.
 
===Influencing residential electricity consumption with tailored messages: long-term usage patterns and effects on user experience<ref>Schrammel, J., Diamond, L. M., Fröhlich, P., Mor, G., & Cipriano, J. (2023). Influencing residential electricity consumption with tailored messages: long-term usage patterns and effects on user experience. ''Energy, Sustainability and Society'', ''13''(1). <nowiki>https://doi.org/10.1186/s13705-023-00386-4</nowiki></ref>===
Persuasive technologies are an important method to alter consumer behaviour next to financial benefits.  Personalized persuasive technologies work better to stimulate people into doing what is wanted of them. Things like tailored information, personalized content, cooperation and competition are known to be good design principles. Messages to the user should be send at appropriate times and should be managed to not create irritation.  
 
In a trial done with real household many insights were found by the authors. The program where users could gain more insights next to only SMS messages stating the best time to turn on the appliances was used quite little by the users, the users that did at first often use it proceeded to use it less as the trial went on. The users were happy with being able to see achieved savings as well as being able to see a comparison between real and optimal consumption curves. Users say that an in-home display could have stimulated them even more. Also, next to time and possible savings the rate should also be included in the message sent to the users. It was seen that the washing machines and dryers were shifted most often to accompany electricity savings, while dishwashers the least. Users again brought up a preference for automation. Users finally also mentioned that while they were willing to alter their behaviour it was often hard to do this due to work or other unavailability.  
 
The authors state that their personalized approach did not lead to a higher willingness to use the program than other untailored approaches used in other experiments. The schedules of the users could be taken into account to better approach users for effective savings. If the savings are only very small other incentives should be possibly used such as a widespread reduction in CO2 emissions.
 
===Integration of electric vehicles in smart grid: A review on vehicle to grid technologies and optimization techniques<ref>Tan, K. M., Ramachandaramurthy, V. K., & Yong, J. Y. (2016). Integration of electric vehicles in smart grid: A review on vehicle to grid technologies and optimization techniques. ''Renewable & Sustainable Energy Reviews'', ''53'', 720–732. <nowiki>https://doi.org/10.1016/j.rser.2015.09.012</nowiki></ref>===
Bidirectional V2G, vehicle to grid, has many advantages such as reduce power grid losses, prevent power grid overloading, minimize emissions, maximize profits and help intermittent renewable energy. The drawbacks however are battery degradation, the need for more complex hardware, a high investment cost and social barriers, because people want their car to be charged in case of emergency. Next to cost advantages the vehicle to grid also gives the owners backup power in case of a blackout. The article states that due to battery degradation V2G has to either be completely avoided or correctly optimized in order to only discharge the batteries slowly and till at max 60 percent of full capacity. This maximal percentage also works well taking into account social barriers where car owners want a certain battery level at all times. Finally, the authors state that an incentive-based system is needed for V2G to be taken up by many electric vehicle owners.  
 
===The effects of household automation and dynamic electricity pricing on consumers and suppliers<ref>Miletić, M., Gržanić, M., Pavić, I., Pandžić, H., & Capuder, T. (2022). The effects of household automation and dynamic electricity pricing on consumers and suppliers. ''Sustainable Energy, Grids and Networks'', ''32'', 100931. <nowiki>https://doi.org/10.1016/j.segan.2022.100931</nowiki></ref>===
The article states that the amount of savings done by household automation depends on the household's energy consumption and production through the day. It also depends on the size of the household how much savings can be done and if they are done at all. The presence of solar panels can in fact lead to less profits for a single person household. The automation of households leads to savings in both TOU and RTP pricings, with more savings with RTP. Especially profits made with solar panels can be a great incentive for the households. The suppliers however have a decrease in profits due to the increase in solar panels and automation of the households. Thus, suppliers should tailor their contracts to the consumers if the suppliers want to maximize their own profits.  
 
===Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark<ref>Lago, J., Marcjasz, G., De Schutter, B., & Weron, R. (2021). Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark. ''Applied Energy'', ''293'', 116983. <nowiki>https://doi.org/10.1016/j.apenergy.2021.116983</nowiki></ref>===
This paper sets out to find the state-of-the-art electricity price forecasting models, it describes problems that make comparing of different models hard and also state that there is no clear benchmark to check the performance of models to. The paper states that there are three main models, statistical models, machine learning models and hybrid models. The comparison of these 3 is very hard thus leading to the authors stating not one single state-of-the-art method but choosing multiple. For the statistical models the authors decide that the LEAR model is very accurate, while for the machine learning models the DNN model is most state-of-the-art.  The hybrid models they state to be not compared enough to other models thus they decide to leave them out of consideration.  
 
LEAR stands for Lasso Estimated Auto Regressive, where Lasso is a regression analysis method that performs both variable selection as well as regularization, which is useful to increase the quality of the dataset used for the model. Auto regressive just points to the type of model being based on time series analysis.  
 
DNN stand for Deep Neural Network, which is a type of machine learning with the objective of trying to replicate the way a human brain thinks. The deep part stands for it being multiple levels of machine learning. These models can be better in analysis and prediction than the statistical models but do use much more computing power.  
 
===Residential Demand Response Based on Dynamic Electricity Pricing: Theory and Practice <ref>Dupont, B. (2015, January 27). ''Residential demand response Based on dynamic electricity Pricing: Theory and practice''. <nowiki>https://lirias.kuleuven.be/1731280?limo=0</nowiki> </ref>===
This paper is very long but gives a few good insights into dynamic pricing, it shows that dynamic pricing does indeed reduce the load of mid-peak and peak power plants over the year, while also adding that the peak power plants have to indeed be turned on less due to dynamic pricing. The dynamic pricing also apparently works best when there is already a large amount of renewable and uncontrollable sources of energy.  
 
Further it states that correct meters are needed in household in order to make sure that the electricity need is measured correctly for every hour such that the dynamic pricing can work well. The addition of online monitoring, graphic user-interfaces and in-house displays can help provide useful information to make dynamic pricing work more efficient. The automation is also a wish of households such that dynamic pricing is correctly triggered, and it does not affect the comfort of people, it also makes the use of dynamic pricing more reliable leading to a better integration of the demand spreading. Finally, the author states that substantial savings can be made but this depends on if an electric vehicle is involved and other factors. A balance has to be found between prices and practicality.
 
===Demand side management of industrial electricity consumption: Promoting the use of renewable energy through real-time pricing<ref>Finn, P., & Fitzpatrick, C. (2014). Demand side management of industrial electricity consumption: Promoting the use of renewable energy through real-time pricing. ''Applied Energy'', ''113'', 11–21. <nowiki>https://doi.org/10.1016/j.apenergy.2013.07.003</nowiki></ref>===
This paper looks at the effectiveness of demand side management of electricity usage, which is thus using real time pricing, but in the case of large industrial electricity consumers.  The industrial consumer consisting of a highly efficient cold storage were able to decrease their electricity costs dramatically by looking at the day ahead pricing of the electricity. This thus led to an increase in the amount of wind energy that was used since this corresponded to the electricity prices. Another industrial consumer, a manufacturing plant, showed to not benefit from real time pricing due to a very inflexible load profile, thus leading to much electricity use at high load times.  
 
The study/paper found a clear relation between the adaptation of consumers to the dynamic pricing and an increase in the use of renewable wind energy, thus showing that an increase of users of dynamic pricing would often increase the consummation of all the renewable energy. The dynamic pricing must however give a financial gain otherwise the consumer is not rewarded enough for the effort needed to follow the electricity demands.
 
===Electric vehicle charging and discharging scheduling strategy based on dynamic electricity price<ref name=":0">Ren, L., Yuan, M., & Jiao, X. (2023c). Electric vehicle charging and discharging scheduling strategy based on dynamic electricity price. ''Engineering Applications of Artificial Intelligence'', ''123'', 106320. <nowiki>https://doi.org/10.1016/j.engappai.2023.106320</nowiki></ref>===
The paper set out to make a deep learning algorithm capable of most optimally charging an electric vehicle depending on the dynamic electricity prices. It states that the use of this algorithm more evenly spreads the charging of the vehicles to low-peak times while still having the requirement for the vehicles to always be fully charged. The use of the algorithm led to more robustness in the electricity usage since the addition of more vehicles would not lead to a huge increase in peak loads. The algorithm used also led to big savings for the users saving them much money on the charging of their electric vehicles, while also making more use of renewable sources of electricity. The authors state that the algorithm also takes into account the flexibility of charging and discharging. The paper however does not take into account the degradation of the batteries of the electric vehicles, this should be taken into account since this contributes much to the acceptability of the algorithm. In total though the algorithm used would lead to better spread of electricity usage, better integration of renewable energy and would reduce power grip consumption.
 
===Report of the Quantitative Field Experiment Analysis<ref>Reichl, J. (n.d.). Report of the Quantitative Field Experiment Analysis. ''Horizon 2020 Research and Innovation Programme''. <nowiki>http://www.peakapp.eu/wp-content/uploads/2019/07/D4.1.pdf</nowiki> </ref>===
The main findings found in the paper on the experiment run with households using an informative app on electricity usage and prices, sending a part of the households a message when the price was lowest, and savings could be most achieved. The research found that a discount message led to an increase in electricity usage of around 1 percent on weekdays and 0.8-2 percent on weekends, where messages stating environmental reasons may elicit more wanted behaviour. Households which used the app heavily had even higher savings of up to 7 percent.  
 
Peer-to-peer comparison shown in the app led, according to the paper, to a decrease in electricity usage for many households. A gaming functionality which was also added also seemed to contribute to more savings for the households and thus more usage of the available renewable energy. The test also showed that users start out using the app more and after time learn and thus have to use the app less.  
 
The research state that an app needs: a dynamic tariff scheme, frequent and easily accessible information on marginal prices and frequent and easily accessible information on the household's electricity consumption. Finally, they state that the consideration of ‘smart’ appliances should be taken into account since automation can play an important role in the future.  
===Analyzing the impact of dynamic electricity prices on the Austrian energy system<ref>McKenna, R., Abad Hernando, D., ben Brahim, T. S., Bolwig, S., & Cohen, J. (2019). Analyzing the impact of dynamic electricity prices on the Austrian energy system. <nowiki>https://rucforsk.ruc.dk/ws/portalfiles/portal/81003156/PEAKapp_report_Deliverable_5_2_final.pdf</nowiki></ref>===
The paper has looked into the extent that an app has effect on making a large group of test households adapt to dynamic electricity pricing. The authors looked at how flexibility of the electricity demand can have an effect on electricity usage in both a case with the use of an app stimulating correct behaviour and without such an app. The use of more elastic electricity usage led to a decrease in total system costs and led to an increase in the renewable energy usage. Although comparing to a fully renewable energy focused setup the elasticity of the groups led to a bit more carbon emissions. The use of the app led to an increase of 0.6 percent in total reduction of the system costs, going from 2 percent to 2.6 percent. The elasticity in the electricity use could in some cases lead to more peaks and less smoothness, these peaks however are not on prime times and would not lead to a large need for more fossil fuel-based electricity sources.  
 
===My Phone and Me: Understanding People’s Receptivity to Mobile Notifications<ref>Mehrotra, A. K., Pejović, V., Vermeulen, J., Hendley, R. J., & Musolesi, M. (2016). My Phone and Me: Understanding People’s Receptivity to Mobile Notifications. ''CHI ’16: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems''. <nowiki>https://doi.org/10.1145/2858036.2858566</nowiki></ref>===
A paper from 2021 reporting on a quantitative research using a mobile phone app researches the behaviour of mobile phone users when interacting with notifications. A. Mehrota et al. constructed a mobile app that monitors mobile phone notifications, the way the users interact with these notifications and why they chose to act like they did. They app measured the following: The time it took for the notification to be seen by the user, or the "seen time"; The time between the user seeing the notification and acting on, or the "decision time". The following was asked in a survey, which the users would sporadically receive and fill in: The way the users would respond to these notifications; The reason why they acted like they did; The complexity of the task they were performing while receiving a message; The effect the notification had on the user; The general personality traits.
 
The paper found several connections between these measurements and/or surveys, such that it would be too much to mention all of them in this summary. Because of that, we will be listing some connection that were found in this paper which are useful for our own project: Users would see a notification quicker when performing a more complex task: Users would experience more disruptiveness from a notification when performing a more complex task; Users would open the notification, despite performing a complex task, if the notification contained useful information; Users would experience more disruptiveness when receiving a notification when they almost finished a task.
 
===Most important remarks in paper on the preferences for dynamic electricity tariffs<ref>Buryk, S., Mead, D., Mourato, S., & Torriti, J. (2015). Investigating preferences for dynamic electricity tariffs: The effect of environmental and system benefit disclosure. ''Energy Policy'', ''80'', 190–195. <nowiki>https://doi.org/10.1016/j.enpol.2015.01.030</nowiki></ref>===
Stating the most important remarks from the article it is found that, consumers want on average a 12.22 percent reduction in costs to switch from a fixed tariff to a dynamic tariff, where males need an extra 10 percent. People who stated it to be easy to spread load to low load moments were more likely to switch to a dynamic contract, which of course makes sense. Next to ease of use the environmental considerations also led to quite a large percentage of people being willing to switch to dynamic tariff.  
 
Thus, indeed taking environmental and system benefits into account, where system benefits are in the form of load reduction on the net, led to people being more willing to switch from a fixed contract to a dynamic contract. This environmentally conscious group of people were 10% more likely to switch. Next to environmental considerations the ease of use was also very important showing a need for automation of load usage.  
 
===Important findings in paper on household energy rules and activities during peak demand<ref>Stelmach, G., Zanocco, C., Flora, J. A., Rajagopal, R., & Boudet, H. (2020). Exploring household energy rules and activities during peak demand to better determine potential responsiveness to time-of-use pricing. ''Energy Policy'', ''144'', 111608. <nowiki>https://doi.org/10.1016/j.enpol.2020.111608</nowiki></ref>===
This article used a survey to inquire into people's willingness to shift their load usage to other times for different electricity using activities in the house. During peak times the activities most households took part in were watching TV, cooking with an oven/stovetop, using a computer/laptop/game console and using lights. From the people who answered that they did these activities, which ranged from 80-90 percent of respondents only around 20 percent of them state to be willing to shift these activities. Activities only 40-50 percent of people said they did during peak times were showering and running the dishwasher, clothes dryer and washing machine. For these activities however almost all respondents were willing to shift the time of use to another time where the load is less, except for showers where only half of the peak time showering correspondents were willing to shift. Finally, the survey showed that one third of electric vehicle owners charged their vehicles during peak times, of these again almost all were willing to shift their time of load.  
 
Another founding made was that households with more smart devices were more willing to shift their electricity usage, pointing at a possible link between more smart devices and an increase in interest in the electricity usage of these people. This however did according to the authors not have a link to automation associated with smart devices since this, according to them, still remains an open question.
 
===Summary of paper on the costs and benefits of real-time pricing<ref>Zethmayr, J., & Kolata, D. (2018). The costs and benefits of real-time pricing: An empirical investigation into consumer bills using hourly energy data and prices. ''The Electricity Journal'', ''31''(2), 50–57. <nowiki>https://doi.org/10.1016/j.tej.2018.02.006</nowiki></ref>===
This paper looks into the possible savings which can be made by switching to a dynamic contract, where they use smart meter data from homes in the USA. The paper states that even if consumers do not shift their electricity usage 97 percent of the consumers would still have saved money when switching to a dynamic contract. The average savings for a consumer turned out to be around 87 dollars annually. The small fraction of consumers who would have seen their costs go up it would only have gone up by around 5% compared to their annual bill.  
 
The data collected showed that while, as expected, consumers with a more even load profile saved more money with a dynamic contract, more unexpectedly even consumers with a peakier load profile saved money with the dynamic contract. The paper does state that these savings could partially be attributed to the fact that the rate was influenced by events in 2014 and it could therefore be the case that normally the two contracts would be more similar in costs.  
 
===Towards flexible energy demand – Preferences for dynamic contracts, services and emissions reductions<ref>Ruokamo, E., Kopsakangas-Savolainen, M., Meriläinen, T., & Svento, R. (2019). Towards flexible energy demand – Preferences for dynamic contracts, services and emissions reductions. ''Energy Economics'', ''84'', 104522. <nowiki>https://doi.org/10.1016/j.eneco.2019.104522</nowiki></ref>===
This paper investigates the households acceptance of hypothetical flexible energy contracts, aiming to increase the demand side flexibility. The results indicate that their sensitivity to electricity restrictions is higher than the sensitivity to heating restrictions. Furthermore, households require a considerable compensation to choose real time pricing over fixed fees. Next to that, other incentives such as CO2 reduction could incentivize flexibility on the demand side.
 
The articles concludes with: "There is a clear need in the market for automated smart home technologies that can optimize consumers’ heating or electricity consumption in such a way that no direct action from the user side is needed. Our results suggest that households are willing to participate in smart load control services; however, at the same time, they require compensation for the associated discomfort." According to our findings, the load control of heating is likely to be the low hanging fruit because the required compensations were moderate and distinctly lower relative to the respective compensations for the load control of electricity usage. Space heating also corresponds to a considerable share (approximately 70%) of the total residential energy consumption (Official Statistics of Finland, 2016b) and, hence, has the highest potential for demand side flexibility.
===Large-scale assessment of mobile notifications<ref>Shirazi, A. S., Henze, N., Dingler, T., Pielot, M., Weber, D., & Schmidt, A. (2014). Large-scale assessment of mobile notifications. ''CHI ’14: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems''. <nowiki>https://doi.org/10.1145/2556288.2557189</nowiki></ref>===
In this paper a large scale assessment of mobile notifications is conducted. This was done through an application that enables users to connect with the computer of the user. The outcome showed that messages from different categories were valued differently. Messages from communication apps and calendars were valued high. This might be useful for our project in the sense that our messages could certainly be high-valued, since they contain information consumers would want to know. However one of the findings was also that the nature of notifications is disruptive. This is because they distract from the tasks at hand. Furthermore it is said that there is no good regulation for app developers to prevent to many messages. The apps are free to create notifications. This could result in too many messages since the app developers are stakeholders and could have as a goal to capture the users attention as much as possible. However we do not have this as our goal, since we don't benefit from repeated use of our app. This is therefore not a problem concerning our messaging.  <br />
 
==Hypotheses==
 
 
The hypotheses are as follows:
 
#Our product will keep electricity costs of households with a dynamic contract below the electricity costs the same households would have with a variable contract, assuming the contract is started at the time of our project.
#Our product will keep electricity costs of households with a dynamic contract below the electricity costs the same households would have with a fixed contract, assuming that the contracted is started at the time of our project.
#Our product will reduce the network congestion caused by households.
#Our product will reduce the green energy wasted due to differences in supply and demand on the energy network.
#Our product will make sure households will make use of green energy rather than fossil fuelled energy.
 
===Approach===
On this wiki we will document the different ways in which these hypotheses will be tested. The first two hypotheses will be tested by comparing the energy costs of a consumer using our product with a dynamic to the energy costs of the same user having either a variable or fixed contract. This will be calculated for different scenarios to get a concise outcome of these comparisons. Hypotheses number three through five will be tested by performing literature study and interviewing involved parties.
 
==Deliverables==
The deliverables of this project will exist of a mobile phone application which will tell users when to turn on their electric appliances in order to get the lowest possible energy cost. The app will achieve this by checking for the dynamic energy pricing, having knowledge of all the electric appliances available and calculating for each appliance when it should be turned on, keeping in mind the duration that a device needs to stay on. To properly keep track of what requirements the app needs to fulfil, what requirements we would want it to fulfil and by what the developmentof the product will be constraint by we constructed three lists in which these points will be addressed.


====Requirements====
===Requirements===


*Safety
*The product needs to be connected to the internet in order to know the energy prices of the coming twenty four hours;
**
*The product needs a list of electrical devices along with information about the manner in which they consume energy (i.e. a dishwasher uses electricity in a cycle of a few hours, while a freezer needs to be on the entire time);
*User feedback/interaction
*The product needs an algorithm that optimizes the time of use for each device in its device list;
**App should be easy to use
*The product needs an environment which the user can interact with (e.g. an application on a mobile device, a website or a device of its own);
*Implementable
*The product needs to be able to send information to the user by either notifications or a screen, or the product should be able to send signals directly to the devices which should be turned on/off;
**Relatively cheap
*The user needs to be able to input information about their preffered time of receiving notifications (should this be applicable);
**No infrastructural changes in the electric circuit for a device that is able to turn on electric device
*The product needs to show the money that was saved in comparison with a variable electric contract;
**Algorithm should be reliable
*The user should not feel disrupted by the app (e.g. the app should not send notifications that would disrupt the user);
*The environment in which the user interacts with the product should be easy to navigate, such that users with every level of experience with technology should be able to know what they are telling the app and what the app is telling them (e.g. old people, who generally have a lower level of experience with technology should not be confused by the environment).
*
*


====Preferences====
===Preferences===


*App should be easy to use
*The electric devices for which the product will optimize their electricity use should all be smart devices, such that the user does not need to turn on the devices manually;
*App is looking good
*App tracking the total amount of saved money


====Constraints====
===Constraints===


*Environment (house)
*The great majority of houses are, as of now, not (fully) equipped with smart devices, which means that our product should send notifications to the user telling them the time at which they should have the electric devices turn on;
**Not-smart electric devices
*The users will not always be close to their homes and their devices at the optimized time for their devices to be turned on. This leads to multiple constraints: the user needs to have their devices should have prepared all devices in advance before they go away from home; The devices should all either have an option to schedule the time they turn or should be smart devices.
**Times when the electric devices cannot be turned on, a not ready dishwater for example.


==Users==
==Users==
The possible users for the algorithm for low energy pricing are quite vast, ranging from private homeowners to businesses. Private homeowners can use this app to lower their expenses on energy, which is especially important due to the surge in energy prices due to everything happening in geopolitics. Homeowners could thus use this to turn on their appliances at the right times leading to huge chances on saving large sums of money. Next to private homeowners even factories or businesses could look into using the algorithm, their sometimes-intensive use of energy could then also be better placed at more beneficial times. Energy intensive procedures needed to for example fabricate a certain product could then be done at better times lowering the costs of production leading to higher profits, which is in capitalism of course one of the main drivers in business. Thus, the users for the algorithm spread almost everyone, since almost no one lives without using electricity in this current era.   
The possible users for the dynamic pricing support system, which will most likely be in the form of an app, are vast, ranging from private homeowners to businesses. Private homeowners can use this app to lower their expenses on energy, which is especially important due to the surge in energy prices due to everything happening in geopolitics. Homeowners could thus use this system to turn on their appliances at the right times leading to huge chances on saving large sums of money. Next to private homeowners even factories or businesses could look into using the algorithm, their sometimes-intensive use of energy could then also be better placed at more beneficial times. Energy intensive procedures needed to for example fabricate a certain product could then be done at better times lowering the costs of production leading to higher profits, which is in capitalism of course one of the main drivers in business. Thus, the users for the system spread almost everyone, since almost no one lives without using electricity in this current era.
 
===Stakeholders===
 
====Private homeowners====
As described above one of the most important users would be private homeowners, since the developed system would enable them to save money. This users most important wish would encompass mostly a good working app which is easy to navigate as well as a trustable system behind the app. The system should be trustable and give correct calculations on savings as well as correct times of prime use, since if the system is wrong often there would be no need to use the app. Some errors can be accepted though since in no case would using the app cost more money than when using electricity on chosen times, only a perfect human being would be able to spread the electricity usage better than the system.
 
====Companies====
The companies would similarly to the homeowners also want a good working app, with again a focus on the accuracy of the system. Companies would also benefit from other built in functions such as overviews of electricity use as well as notifications in order to inform people of the optimal usage times. Companies could in theory thus make profit from the use of the system but would not need major alterations to the normal version for private homeowners, only the inputtable devices should be customizable since companies would use machines that use amounts of electricity which are not widely known. 
 
====Other institutions====
Since the use of electricity is such a general thing in life, any group of people, company or institution having control over their electricity use could use the app to lower their electricity prices. An important question that then arises however is, in the case that many people start using the app, can the system adapt to correctly spread the use of electricity. Since the increase in usage would also lead to an increase in electricity use on times that would otherwise be seen as off peak, this would then have to be counteracted by an increase in renewable energy sources. This increase would balance out the increase in load on the off peak times, leading to a greener society.
 
====Electricity companies / Network operators====
The electricity producers and network operators are also users that have to be taken into account when it comes to the system. While they may not be direct users of the system it is in theory of their best interest to have as many consumers use the system since this would lead to an increase in dynamic electricity pricing users and make the existing users more efficient. This would then lead to a decrease in load on the net on peak times and a more even spread which is positive for the network operators. The electricity companies would also have to spend less on expensive sources of electricity such as gas to fulfil peak demands since cheaper forms of electricity like solar and wind would be better used during low peak times, it could however also lead to a decrease in profits for the electricity companies since consumers can save money by using the system, if this is outweighed by the decrease in cost of electricity sources is unknown but is very important to determine the stake that electricity companies have in this system. If it would decrease total profits the electricity companies may be inclined to increase dynamic contract prices which in turn could lead to less dynamic contract users, it should thus be optimally balanced in order to have as many consumers switch to dynamic contracts. 
 
====Government====
The government is also a stakeholder in the to be designed system, since the system would in theory lead to a better spread of electricity usage as well as a more optimal usage of renewable electricity sources. This increase in use of green electricity is what the government, at least with the current political parties, wishes since due to European laws the decrease in carbon emissions has to take place. This decrease in carbon emissions is thus caused by the better implementation of dynamic contracts which lead to a more optimal use of renewable electricity sources, where more of these sources would also further increase the efficiency of the whole electricity grid. Since renewable electricity is in the long run often cheaper it could also lead to financial benefits for the government since fossil fuel subsidies would no longer be needed.
 
===Scenarios===
In order to get a better view on the typical electricity usage of possible users of our product we sketched two different scenarios: A single family household consisting of two adults and one child, and a single person houshold.
 
====Single family household consisting of two adults and one child====
They turn on the dishwasher, dryer and washing machine 1 time per day. They have no electric car and no solar panels. Between 20:00 and 1:00 the TV is on. From 21:00 to 24:00 the child runs a Playstation 5. The freezer and fridge are on at all times. There is no miscellaneous electricity use in this perfect scenario.   
 
A fixed contract would at the moment cost around 0.345 euro per kWh, a variable contract would cost around 0.37 euro per kWh. For the dynamic contract the prices of September 28 were used, these prices are in comparison to other days quite poor for dynamic contract consumers. 
 
Let's say that the household has appliances that are quite average when it comes to electricity use. The Samsung dishwasher is always run on the eco program leading to a use of 1.053 kWh for 195 minutes, where we will assume that this is spread evenly over the time. For the dryer it is assumed that a ‘mix’ cycle is always used and that the data from 2016 is accurate for the dryer owned by this household. Thus, the dryer will use 0.66 kWh over an assumption time span of 2 hours. For the washing machine it is assumed that 0.8 kWh is used, spread also over 2 hours. For the TV it is assumed that it uses 0.2 kWh per hour. The PS5 with TV combo uses 0.3 kWh per hour. The fridge uses 0.015 kWh per hour and the freezer also uses 0.015 kWh per hour.  For the dynamic contract it is assumed that the appliances are turned on at the best possible times and that the electricity use of appliances is spread evenly over the time.   
 
Adding the entire electricity usage up found is a total use of 5.11 kWh. Thus, the cost of this electricity usage can be easily calculated for the fixed and variable contract. With a fixed contract this electricity use would cost the household 1.76 euro and with a variable contract it would cost 1.89 euro. For the dynamic case the calculation is a bit trickier since the price is different for every hour. In order to spend the least amount of money with the dynamic contract with the electricity prices of 28 September the dishwasher should be turned on at 02:00 thus it is assumed that the appliances in this case are ‘smart’ enough to be turned on automatically. The dishwasher uses 0.0054 kWh per minute. The costs of running the dishwasher from 02:00 to 05:15 is then 0.279 euro. The cost of the washing machine turns out to be 0.208 euro, for the dryer 0.172. The TV is run during fixed times and would cost for this specific day 0.332 euro. The PS5 and TV combo would cost 0.297 euro. For the fridge and freezer, the total cost is 0.228 euro. Thus, the total cost with the dynamic contract is 1.52 euro. This would mean that on a very average day the savings are 0.24 euro in comparison to a fixed contract and 0.37 euro in comparison to a variable contract. Which, assuming this is an average amount of savings, would save this household 87.6 euro on year basis comparing to a fixed contract and 135 euro comparing to a variable contract.   
 
====Single person household====
This person lives alone in a small apartment, the dishwasher, washing machine and dryer are turned on once every 3 days. This person also owns an electric car which is used for commuting from and to work, needing to only be charged a small amount at home since most of the charging is done while the person is at work. In this case the electric car is a Tesla model 3 which needs to be charged 20 percent during the night. The person also powers on a TV from 20:00 to 24:00 and cooks using induction for 30 minutes at 18:00. The electricity pricing data from Oktober 1 is used where it is assumed that today is the day the person turns on their appliances.  
 
The person uses a washing cycle costing 1 kWh spanned over 2 hours; the dryer is put on the ‘mix’ cycle and uses 1 kWh since this dryer is quite old and thus less energy efficient. For the dishwasher the quick cycle is used, using 0.75 kWh in 30 minutes.  Charging the Tesla 20% will cost 10 kWh, which takes approximately 1 hour. The induction cooktop will use 0.75 kWh. Finally, the TV will use 0.15 kWh per hour.   
 
For the fixed contract a price of 0.345 euro per kWh is taken and for a variable contract 0.37 euro per kWh. The total kWh consumption is 14.1 kWh, which costs with the fixed contract 4.87 euro and with the variable contract 5.22 euro.  
 
Choosing the best time for the dynamic contract we assume that the appliances are ‘smart’ and can thus be turned on at all times during the day. The charging of the car however has to take place during the night. The appliances are turned on at 13:00, leading to a cost of 0.51 euro for the dishwasher, dryer and washing machine. The cooking would cost 0.25 euro and the TV would cost 0.2 euro. Finally, the charging of the car will be done at 03:00 leading to a cost of 2.9 euro. This is in total a day cost of 3.86, which saves 1.01 euro in comparison to the fixed contract and 1.36 euro in comparison to the variable contract. This can however be highly influenced by geopolitics and the weather.  
 
==The App==
 
===The design===
[[File:Desing app.png|thumb|The design board. On this canvas the ideas and the first design prototype of the app was made. |600x600px]]The prototyping of the design was done in Figma, a vector graphics editor specialized in web and mobile applications. This part of making the app is important because it decides how the user interact with the features of the app. If the designing of the app is not done properly then it will be difficult for users to learn and use the app as intended. A lot of thought went into making the app as simplistic with still having all the requirements that were set. The final design exists of four main pages, Home, Devices, History and Settings, the user can switch between these pages via the bottom navigation bar.
 
====The Home-page====
On the Home-page a graph with the prices for every hour is showed in a central place with the minimal, average and maximal prices. This is done so that the user can in a glance see what the energy prices for that day are. There is also at the bottom of the Home-page some place where the information of the next planned activities are shown such that the user is able to see what is going to happen and if necessary to quickly see if something needs to be changed. To finish the Home-page of a nice vector image of an house is placed at the top to make the app feel more connected to the user and to give a nice first impression of the app when opened.
 
====The Devices-page====
The Device-page shows all the added devices in a list. Each device starts off with an icon of the device and next to it the model name. Every device has an selection panel with the available cycles. The cheapest cycle is displayed in a green text colour, when a cycle is not available the text colour will be grey otherwise it will be white. Below the cycles the start and endtime is shown for the timeframes that the user has selected. Below the times a short text is shown with the cheapest starttime, or in case that that is available a text will be shown why the algorithm can not calculate the time. Next to this text is a button so that the user can indicate that he will use/set the selected cycle at the calculated time. This button also saves this information with the cost and can be seen in the History-page.
[[File:Screenshots.png|thumb|600x600px|These are screenshots of the app. The first screenshot is of the Home-page, second from the Device-page, third from the History-page and the last from the Settings-page.]]
 
====The History-page====
The History-page has at the top an overview of the total consumed energy, the average price per kWh, the total cost of the electricity and the money saved in comparison to a variable contract. below that is an overview with the same information but than categorized per device. This is done so the user can see what devices use the most energy or are on average more expensive per kWh, these insights can be useful for the user to decrease the energy consumption and minimize the money that he spends on electricity.
 
====The Settings-page====
The Settings-page exists of multiple tabs to improve the navigation through the settings. On the first tab some general settings can be adjusted and a link to the device-settings. In the device-settings it is possible to add a device by selecting what the device is and by typing in the model of the device. When a device is added the cycles that the user want can be selected and the preferred timeframe can be set. There is also an option to set the starttime of the timeframe to now such that the user does not need to change the time if they are not sure at what time the device is always ready to start. In the settings it is also possible to change the name of a device in case of a mistake with naming the device or in case when an old device is changed with a new one. It is also possible to delete a device from the app when the wrong device was added or when the user no longer uses the device. The tabs can be navigated by clicking on the element in a list and by the icons at the top of the tab.
 
===The technical challenges===
 
====Dynamic design====
One of the challenges of building an app were the user can add devices is that the app needs to be dynamic. Programming a dynamic interface is more challenging than just a static page, because the looks of the app wil change according to the added devices and selected settings. This meant that the app had to store the devices and settings that the user added, this is not possible to do in the codefile self. The reason for this is that the code is always the same and gives the same result every time the app is turned on, the solution for this is to store all the devices and settings in a separate database to save the information. The code then reads the database, and dependent on the information in the database, and shows the appropriate information. This database also needed to be adjusted by the user from inside the app, this meant that the user needed to be able to add new devices, edit devices and delete devices. There is also the challenge that a dynamic design is more sensitive to errors, because there are a many different configurations that the user can create by selecting and adding different devices. All these configurations needed to be tested for errors/ unwanted behaviour and changed appropriate. Because of this reason is it also not possible to know for sure that the app has no errors because only the most common configurations were tested during the limited time.
 
====The algorithm====
Another challenge was implementing the algorithm in the app in a user friendly way. The main problem was that the selected timeframe was not always possible in the sense that the algorithm was not useful when the start time was already over and that the information for the next day only came available at 13:00. This meant that a smart algorithm needed to be implemented to filter out the timeframe that would lead to an error and notify the user why it was not able to calculate the best price (error prevention). And adjust the timeframe to the next day when the timeframe was already over for this day. The algorithm also had to know that when the endtime is earlier than the starttime that the user meant that the endtime is for the next day.
 
When a possible timeframe was put into the algorithm it had to calculate when the best starttime of the selected cycle of the device is. This is calculated by taking the correlation of the energy prices per hour and the energy usage of that particular cycle. This gave the cost for the cycle at every moment in time, we then used a simple minimum function to find the first cheapest price and the corresponding time.
 
Another feature of the app is that it shows which cycle is the cheapest in the selected timeframe. This is calculated by running the algorithm on the selected cycles and finding the minimum value of the all the cycles and colouring that name green. The challenge that this gave was that on opening the device-page the algorithm had to run on average three times per added device. This meant that when a user added five devices it needed to run almost fifteen times. This was a problem because the algorithm took approximately 0.8 second per device to calculate the cheapest cycle, this would lead to a load-time of four seconds to open the device-page. To solve this problem a more efficient version of the algorithm was implemented that used a convolution algorithm and flip the energy usage function of the cycle. This is in principle the same as a correlation function, but because calculating the convolution is more optimized yielded this a speed improvement of 200 times. This improvement was enough to not notice the added delay of running the algorithm multiple times.   
 
 
==User interviews==
 
====Interest interview====
At the start of our project we interviewed a user of a dynamic contract, which can be found in Appendix B.2. Before this interview we let the interviewee sign an informed consent about the interview, which can be found in Appendix B.1. In this interview we found that he would be interested in the product, that it could be a problem that devices should be prepared at all times to be turned on at the right time and that he uses a dynamic contract because both economic and environmental reasons. We realized that we should be more specific in our interviewing style however, after we gained some more information on our product - by literature study, brainstorming and working towards a usable product -, which is why we only conducted this interview once
 
====App testing====
To get some insight into the last two requirements of our app, regarding disruptiveness and usability, we created the following interview. Herein we first inform the user about concept of dynamic contracts and its workings. After that we give the user some tasks which they should complete, to test if they can properly use the app. Would we experience that the user can not complete a part or all the tasks, we would possibly tell them an additional explanation. And at last we ask the interviewee some questions about their opinion on the app and its notifications. Before conducting the tasks and the interview we let the interviewees sign an informed consent form, which can be found in Appendix B.1.
 
Because we will only be interviewing Dutch people, the explanation and additional information, will first be stated in Dutch, followed by a translation.
 
=====Short explanation beforehand=====
in Dutch:
 
Een dynamisch contract is een vorm van energiecontract waarbij (in het geval van de Nederlandse variant) één dag van tevoren de energieprijzen voor de volgende dag worden vrijgegeven. Deze prijzen verschillen per uur, waarbij de prijzen vaak hoger liggen bij rond piek uren waar mensen over het algemeen veel stroom gebruiken - denk aan de tijd rond het avondeten -, maar waar de prijzen stukken lager - of zelfs negatief zijn, zodat u zelfs geld terug kan krijgen! - rond de tijd waarop weinig stroom wordt gebruikt en veel groene energie wordt opgewekt. Met onze app maken we het makkelijk om dit dynamisch contract optimaal te gebruiken, zonder zelf veel na te hoeven denken over wanneer je je apparaten aan moet zetten.
 
In English:
 
A dynamic contract is a type of energy contract where (in case of the Dutch variant) the energy prices are published one day before they apply. These prices change from hour to hour and are most likely to be higher than average at times when people generally use more electricity - around dinner time for instance-, but could be way lower - or even negative, meaning you actually get money back! - around times when generally not much energy is being used or at times when a lot of green energy is being generated. With our app we try to make it easier for consumers to optimally use this dynamic contract, without needing to remember when they have to turn on their devices themselves.
 
=====Additional information about the tasks=====
[[File:Screens.png|none|thumb|From left to right: Home; Operations; History; Settings; Settings->Devices]]
In Dutch: (we will still be refering to the screen names in English in the descriptions between brackets for the interviewer only)
 
(Terwijl het Home scherm wordt laten zien)
 
Dit is het algemene scherm. In de grafiek kunt u zien op welk moment de energie prijs het hoogste of laagste is. Aan de tekst erboven kunt u zien tot hoe hoog of laag deze prijs gaat en wat het gemiddelde is van de energieprijs vandaag. Het plaatje van het huisje is er enkel ter decoratie.
 
(Terwijl het Operations scherm wordt laten zien)
 
Hier ziet u een overzicht van je apparaten, met elke wat knoppen er onder. Met deze knoppen kunt u checken welke modus u wilt gebruiken en door op activate te drukken geeft u aan dat u een melding wilt ontvangen over het juiste moment om dit apparaat in te stellen. In de huidige versie op de computer kunnen de "start-" en "end time" niet worden aangepast, dit is mogelijk in het Settings-> Devices scherm, waar ik zo meer over vertel.
 
(Terwijl het History scherm wordt laten zien)
 
Hier ziet u een overzicht van de apparaten waarvan het gebruik is gepland. Hier kunt u zien hoeveel stroom het apparaat heeft gebruik, het gemiddelde tarief per kilowattuur, de totale kosten die het apparaat heeft gemaakt en het verschil tussen de kosten die het apparaat heeft gemaakt en de kosten die zouden worden gemaakt als het tarief altijd zou zijn als het gemiddelde.
 
(Terwijl het Settings scherm wordt laten zien)
 
Dit zijn de instellingen. Vanaf hier kunt u gaan naar de instellingen voor apparaten.
 
(Terwijl het Settings->Devices scherm wordt laten zien)
 
Op het scherm voor instellingen voor apparaten ziet u een overzicht van alle apparaten, de apparaat namen en kunt u een nieuw apparaat toevoegen om de lijst compleet te maken. Ook kunt u hier de "start-" en "end time" instellen per apparaat door eerst op het apparaat te klikken of kunt u de naam van het apparaat aanpassen. Na het aanpassen dient u de wijzingen op te slaan door op het icoontje van de floppy disk te drukken.
 
In English:
 
(While showing the Home screen)
 
This is the screen that appears when you open the app.In the graph you can read the highest and lowest daily price. In the text above the exact money for these prices is shown, as well as the average rate of the day. The picture of the house is only decorative.
 
(While showing the operations screen)
 
Here you can see a list of all your appliances, with some buttons underneath each one. With these buttons you can choose the cycle you want the device to run and by clicking activate you let the app know that you would like to receive a message with the right time to turn on this appliance. In the present version of our app on the computer the start and end times can not yet be adjusted, as of now you need to go to Settings->Devices if you want to change this.
 
(While showing history)
 
Here you can see an overview of the appliances of which the usage has been planned. In this overview you can see the amount of electricity used by this appliances during the selected cycle, the average rate per kilowatt hour. the total cost the appliance would have created when the average rate was applied and the difference between that price and the cost that was actually applicable
 
(While showing Settings)
 
These are the settings. From here you can navigate to the device settings.


===Private homeowners===
(While showing Settings->Devices)
As described above one of the most important users would be private homeowners, since the developed app/algorithm would enable them to save large sums of money. This users most important wish would encompass mostly a good working app which is easy to navigate as well as a trustable algorithm. The algorithm should be trustable since if the algorithm is wrong very often there would be no need to use the app, some errors can be accepted though since in no case would using the app cost more money than when using electricity on chosen times, only a perfect human being would be able to spread the electricity usage better than the algorithm.


===Companies===
Here you can see an overview of all your appliances, their names and the possibility to add a new one. When you press on one of the devices you have the option to change the start and end times of the appliance and you have the option to change the name of the device. If you want to save your changes you need to press the floppy disk icon.
The companies would similarly to the homeowners also want a good working app, with again a huge focus on the accuracy of the algorithm. Companies would also benefit from other built in functions such as overviews of electricty use as well as a method to make sure the responsible people are informed, which should thus need everyone to be connected to a single account. Companies would thus make profit from the use of the app/algorithm but would not need major alterations to the normal version for private homeowners.  


===Other institutions===
=====Tasks=====
Since the use of electricity is such a general thing in life, any group of people, company or institution having control over their electricity use could use the app to lower their electricty prices. An important question that then arises however is, in the case that many people start using the app, can the algorithm adapt to correctly spread the use of electricity. Since the increase in usage would also lead to an increase in electricity use on times that would otherwise be seen as off peak.


==Deliverables==
#Check what time the hourly energy costs are highest;
The deliverables of this project will exist of an algorithm that is implanted in an app. This algorithm will be substantiated with documentation in which a literature review is embedded and the capabilities/results of the algorithm will be compared with other energy pricing options.
#Check at what time you should activate the dishwasher today;
#Add an electric car to the devices list;
#Check at what time you should charge the electric car today;
#Check how much energy is used by running the dishwasher one time;
#Check how much money you have saved by charching the electric car on time.
 
=====Questions=====
The paper My Phone and Me tells about mobile phone notifications and how users react to this and it concludes some points which might be useful to keep in mind when designing our own product: If we want to make sure the notifications our app sends to the user are seen quickly, acted upon quickly and not cause disruptiveness for the user, the best time to send our notification is when the user has just started running errands. The first reason for this is the complexity of the task: The task may not be that complex, but it is a relatively complex task a user might perform at home, which is where they should perform the actions suggested in our notification, and the complexity of the task will lead to a shorted time that it will take the user to see the notification. The second reason for this is that a user would experience less disruptiveness when receiving a notification at the beginning of a task. One other thing we will need to keep in mind is that the user is most likely to act on our notification when it contains useful information, which is why we should make sure to present some information which is important for the user. In order to test whether this would really be the case however, we conducted the following questions:
 
#How would you feel about a notification that would remind you to check for the best time to turn on your devices?
#How would you feel about a notification for each device at the time that it should be turned on?
#Would a notification reminding you to turn on a device always be effective for you?
#Would a price indication alongside the notification, showing you how much money you would save if you would follow the notifications instructions and turn on a device, increase the effectiveness of the notification for you?
#Would the notifications be more effective for you when they would always arrive when you are already running chores?
#What else could increase the effectiveness of notifications for you?
#Would you experience disruptiveness by the number of notification per day (which is equal to the number of devices listed plus one general notification)?
#What number of notifications would it take for you to experience disruptiveness?
#Would a price indication alongside the notification decrease the level of disruptiveness?
#Would the level of disruptiveness due to the notifications be decreased if the notifications would always arrive when you are already doing chores?
#What else could decrease the level of disruptiveness due to the notifications for you?
 
=====Results=====
In the last weekend before the presentation in which we were going to show our app, we conducted the interview with five interviewees, three of which also tried to complete te tasks. The answers to the questions as well as the results from the tasks are documented in Appendix B.4. From the tasks the following results were found by observing and documenting the behaviour of our interviewees while performing the tasks, their feedback to the tasks and the completion percentage:
 
*Checking when the energy times are highest was found to be very easy and clear.
*Adding a device to the device list was also found to be easy for the most part, although one participant mentioned that it was initially not clear how to save this addition and suggested to add "save" in text next to the floppy disk icon.
*Setting the timeframe for a device went wrong most of the times We already knew that this could be an issue in the version on the computer, which will not be present in the mobile version of the app.
*The meaning of the active button was said to be a bit confusing. This will, again, be fixed in the mobile version, where the possibility of sending notifications is possible.
*The history tab was understood well and was directly clear to all the participants
 
After asking the prepared questions for interviews, and possibly adding related follow up questions, we gained the following results:
 
*All interviewees thought that receiving notifications would help them remember when to turn on their devices in order to optimize the time of their electricity usage.
*Part of the respondents thought a price indication alongside the notification would help increase the effectiveness of the notification, although most did not think it would decrease disruptiveness or at least not when the mentioned amount of money was low.
*Most of the respondents thought it would both increase the effectiveness as well as decrease the disruptiveness of the notifications when the notifications would arrive when the respondents were already running chores.
*The respondents came up with some ideas to increase effectiveness: Create an option where you can select which devices you have already turned on after receiving the notificiton, prompting the app to not (again) remind the user of this device; let the app send a notification when a device has completed its cycle; create an overview of the money saved per month, additionally to the money saved per cycle.
*One interviewee had the suggestion to decrease the disruptiveness of the notifications by setting a custom timeframe when they could possibly arrive, as to - for instance - not wake up the users during the morning in weekend when they could want to sleep in instead.


==State-of-the-art==
==Discussion==
Papers on algorithms for optimal energy consumption.
The results, which are described above, answer only the questions raised after the creation of the application, leading to an analysis of the improvements that could be done to the application. These improvements are thoroughly stated above but can be summarised by the findings that notifications are a very useful addition to the application as well as that the application should be easy to understand since it could otherwise cause users to be unable to even start the use of the system. The other part of the results leads from the extensive literature research that was done, this research showed two main insights: Firstly it showed that the use of dynamic contracts lead to both cost savings as well as emission savings and secondly it showed that consumers benefit from having a type of reminder, which would in the case of the product be an application, or having their appliance use fully automated. The first point thus shows that the dynamic contracts have, in the current day, an important role to play in the reduction of emissions due to the load balancing it enacts on the power grid. The second insight shows that the creation of an application is a viable option in order to get more consumers to switch to dynamic contracts, since often the threshold that consumers find hard to cross is caused by them not wanting to alter their lifestyles too much or simply not wanting to put so much effort into the determination of the times to turn on their appliances. These results matter since it gives a foundation on which the entire idea of the application was build as well as giving insights into what should be put into the application. Without these results the application could not be further developed in the future and would lack any real sense of improvement, thus leading to a recommendation of increasing the applications understandability as well as a correct implementation of the notifications, where most importantly the notifications should be included in order to give consumers the biggest advantage when starting to use it.


*Dynamic energy scheduling and routing of a large fleet of electric vehicles using multi-agent reinforcement learning.<ref>Alqahtani, M., Scott, M. J., & Hu, M. (2022). Dynamic energy scheduling and routing of a large fleet of electric vehicles using multi-agent reinforcement learning. ''Computers & Industrial Engineering'', ''169'', 108180.</ref>
While our user test and literature research thus gave much valuable information towards the effectivity of the app as well as the future improvements that should be done, it does not cover all possible future scenarios. The development of the application was completely based on the availability of a dynamic electricity contract in a world where it is still most normal to have a fixed electricity contract. In this world then consumers could switch to a dynamic contract and save on their costs as well as improving the grids load distribution by using the electricity more on the times that it is made available by renewable sources. If however in the future fossil fuel based electricity is completely fased out a dynamic contract would not add any more advantages for the electricity provider and would thus simply be unnecessary, since in this case it is only renewable energy that is available anyway. A case where the shift to a dynamic contract is made by many people could also influence the dynamic contracts and lead to the application not being valuable anymore from a cost saving point of view for the consumer. Finally the size of the effect this application will have on increasing dynamic electricy usage as well as optimisation is impossible to say from the results found in the tests and literature, thus while it shows that it is likely that use of the application reduces grid load and emissions this is not possible to quantise. Thus in future research on not the further development of the application but on the effects of dynamic contracts this could be taken into account leading hopefully in the future to a better insight to the achievements that can be made with this new way of electricity usage.
*Residential demand response: Dynamic energy management and time-varying electricity pricing.<ref>Muratori, M., & Rizzoni, G. (2015). Residential demand response: Dynamic energy management and time-varying electricity pricing. ''IEEE Transactions on Power systems'', ''31''(2), 1108-1117.</ref>
*Implementation of a dynamic energy management system using real time pricing and local renewable energy generation forecasts.<ref>Elma, O., Taşcıkaraoğlu, A., Ince, A. T., & Selamoğulları, U. S. (2017). Implementation of a dynamic energy management system using real time pricing and local renewable energy generation forecasts. ''Energy'', ''134'', 206-220.</ref>
*Impact of dynamic energy pricing schemes on a novel multi-user home energy management system.<ref>Abushnaf, J., Rassau, A., & Górnisiewicz, W. (2015). Impact of dynamic energy pricing schemes on a novel multi-user home energy management system. ''Electric power systems research'', ''125'', 124-132.</ref>


<br />
==Conclusion==
The goal of the project was to build a system that would lead to consumers being more willing to switch to a dynamic contract as well as consumers with a dynamic contract savings more on their electricity expenditure. This was in order to have an increase in dynamic contract users as well as a more optimized use of the contract which would then lead to better load balancing of the power grid. This load balancing is needed due to the increase in variable green electricity sources, which are on the rise due to the increasing demand for carbon emission reduction. The system build to increase this was an application that lead to the consumer having to put little to no thought into the determination of the optimal times of turning on their electrical appliances, which in turn should lead to more savings due to a better use of the dynamic contract. While the user tests did not yield any data into the capability of the app to actually save consumers money, the tests did lead to insights into the development of the app. The app was well designed but did need instructions in order to correctly use it. Furthermore notifications were said to be wished by the consumer thus leading to recommendations that should be included in a future iteration of the application. From literature research and the set up scenarios it is clear that this application would save almost every household money, since the dynamic contracts are overall less expensive than fixed contracts. For the ability of the application to reduce the load on the electricity grid conclusions have to be made from literature leading to a clear view that this application leading to an increase in use of dynamic contracts would lead to grid load reduction, enabling better and more future use of green energy.


Thus the created application would in a further developed state, where the marketing of the application is done well leading to many users, lead to a decrease in electricity costs for consumers but most importantly lead to a world where implementation of green electricity is much easier. Current ideas of a future where only green energy is available rely solely on large storages of electricity in order to cover peak load needs, but with the increased use of dynamic contracts, in part due to the availability of an application that makes the optimal use of the dynamic contracts easy, the need for these huge storages are much less since the peak load is displaced instead of displacing the electricity. A future where dynamic contracts are used mainly, with the use of the application, is thus a green world where the world is aiming at. <br />
==Appendix==
==Appendix==


===Milestones and logbook===
===Appendix A: Planning and logbook===


====Milestones====
====Appendix A.1: Planning====
{| class="wikitable"
{| class="wikitable"
|+
|+Milestones and responsibilities
!Week
!
!Day
!Week 1
!Date
!Week 2
!Occasion
!Week 3
!Contents
!Week 4
!Week 5
!Week 6
!Week 7
!Week 8
!Week 9
|-
|Decide on subject of the course
|All
|
|
|
|
|
|
|
|
|-
|Preparing meeting agendas and
make minutes
|J
|
|
|
|
|
|
|
|
|-
|-
|1
|State-of-the-art section
|Monday
|L
|04-09
|
|Instruction meeting
|
|Group formation, Brainstorm about subject
|
|
|
|
|
|
|-
|-
|1
|RPC section
|Thursday
|M
|07-09
|
|Group meeting
|
|Decide on a subject, divide tasks among group members
|
|
|
|
|
|
|-
|-
|1
|Deliverables section
|Sunday
|S
|10-09
|
|Deadline
|
|Update the wiki on the first progress
|
|
|
|
|
|
|-
|-
|2
|Logbook and planning
|Monday
|J
|11-09
|
|Feedback meeting
|
|Receive feedback on choice of the subject
|
|
|
|
|
|
|-
|-
|2
|Study on cost difference between
|Thursday*
energy contracts
|14-09
|
|Group meeting
|S
|Brainstorm about what involved parties to contact
|
|
|
|
|
|
|
|-
|-
|2
|Study on the impact of our product on
|Sunday
network congestion
|17-09
|
|Deadline
|J
|Finish literature study, Write summaries of the literature study on the wiki
|
|
|
|
|
|
|
|-
|-
|3
|Study on consumer behaviour
|Monday
|
|18-09
|L/M
|Feedback meeting
|
|Receive feedback on literature study and choice of involved parties
|
|
|
|
|
|
|-
|Ideation of the design of the product
|
|S
|
|
|
|
|
|
|
|-
|Construct the problem statment
|
|
|L
|
|
|
|
|
|
|-
|Interviewing potential users
|
|
|M
|
|
|
|
|
|
|-
|Contact companies involved in network
congestion
|
|
|J
|
|
|
|
|
|
|-
|Finalize the algorhythm needed for the product
|
|
|S
|
|
|
|
|
|
|-
|-
|3
|Summaries of literature studies on the wiki
|Thursday*
|
|21-09
|
|Group meeting
|
|Start working on the product, contact involved parties
|L
|
|
|
|
|
|-
|-
|3
|Make the final decicion on the programming
|Sunday
language used for the product
|24-09
|
|Deadline
|
|Finish contacting involved parties, update the wiki on the first design/idea of the product
|
|
|S
|
|
|
|
|-
|-
|4
|Analyse the interviews of users
|Monday
|
|25-09
|
|Feedback meeting
|
|Receive feedback on contact with involved parties and product
|
|J
|
|
|
|
|-
|-
|4
|Analyse the contact of companies
|Thursday*
|
|28-09
|
|Group meeting
|
|Implement information of the involved parties, work on the product
|
|J
|
|
|
|
|-
|-
|4
|Finalize the app for product testing
|Sunday
|
|01-10
|
|Deadline
|
|Update the wiki on the progress
|
|
|S
|
|
|
|-
|-
|5
|Prepare presentation
|Monday
|
|02-10
|
|Feedback meeting
|
|Recieve feedback about the implementation of the information of the involved parties and current version of the product
|
|
|
|J
|
|
|-
|-
|5
|Discussion
|Thursday*
|
|05-10
|
|Group meeting
|
|Implement the feedback, continue working on the product
|
|
|
|J
|
|
|-
|-
|5
|Future research
|Sunday
|
|08-10
|
|Deadline
|
|Update the wiki on the progress
|
|
|
|L
|
|
|-
|-
|6
|Appendix
|Monday
|
|09-10
|
|Feedback meeting
|
|Receive feedaback on the progress
|
|
|
|J
|
|
|-
|-
|6
|Bibliography
|Thursday*
|
|12-10
|
|Group meeting
|
|Work on the first draft of the final version of the wiki and the product
|
|
|
|L
|
|
|-
|-
|6
|Process feedback on presentation and
|Sunday
finalize presentation
|15-10
|
|Deadline
|
|Finish the first draft of the final version of the wiki and product
|
|
|
|
|J
|
|
|-
|-
|7
|Test the product
|Monday
|
|16-10
|
|Feedback meeting
|
|Recive feedback on the drafts
|
|
|
|M
|
|
|-
|-
|7
|Final presentation
|Thursday*
|
|19-10
|
|Group meeting
|
|Implement feedback, Check each others work, start working on the presentation
|
|
|
|
|L
|
|-
|-
|7
|Process the product test
|Sunday
|
|22-10
|
|Deadline
|
|Finish the final version of the wiki and the product, finish the presentation
|
|
|
|
|M
|
|-
|-
|8
|Finilazation of the wiki
|Monday
|
|23-10
|
|Presentation
|
|
|
|
|
|
|
|J
|}
|}
<br />
<br />


====Logbook====
====Appendix A.2: Logbook====
{| class="wikitable"
{| class="wikitable"
|+
|+
Line 246: Line 713:
|-
|-
|Marijn Bikker
|Marijn Bikker
|
|Introductory lecture, research into problems and possible technical solution, Meeting about subject, writing problem statement and RPC's.
|
|6
|-
|-
|Jules van Gisteren
|Jules van Gisteren
|Preparing meeting (0.5h), Meeting about subject (1h), Creating the logbook and planning (2h)
|Searching for ideas (1.5h), Preparing meeting (0.5h), Meeting about subject (1h), Creating the logbook and planning (2h)
|
|5
|-
|-
|Lin Wolter
|Lin Wolter
|
|Searching for ideas (2.5h), Looking into possible users (2h), Start of literature study with writing of State-of-the-art (3.5h)
|
|8
|-
|-
| rowspan="4" |2
| rowspan="4" |2
|Sven Bendermacher
|Sven Bendermacher
|
|Meeting on Monday (2.5h), Looking at possible devices and how to use (2h), Working at the layout and design of the app (4h)
|
|8.5
|-
|-
|Marijn Bikker
|Marijn Bikker
|
|Meeting with tutors, working together on problem, literature study, meeting, literature research, user study
|
|7.5
|-
|-
|Jules van Gisteren
|Jules van Gisteren
|
|Meetings (2.5h), Literature study (3h), Research on possible parties involved in subject (1.5h)
|
|7
|-
|-
|Lin Wolter
|Lin Wolter
|
|Working in group (2h), Finding research on consumer wishes and summarising (4h), Writing problem statement and some other alterations of wiki (1.5h)
|
|7.5
|-
|-
| rowspan="4" |3
| rowspan="4" |3
|Sven Bendermacher
|Sven Bendermacher
|
|Meetings (3h), Finding energy data on house appliances (2h), Finding information about smart devices connections protocols (2h), Programming a working convolution cost algorithm (3h), starting on learning to code a android app in python (2h).
|
|12
|-
|-
|Marijn Bikker
|Marijn Bikker
|
|Discussing the project, discussing the website, finishing interviews, writing informed consent form, meeting, interviewing. (more hours planned next week)
|
|5
|-
|-
|Jules van Gisteren
|Jules van Gisteren
|
|Meeting on monday (2h), Meeting on thursday+preparations (1.5h), Creating planning (1h), Creating mail to Enexis and processing feedback on the mail (0.5h)
|
|5
|-
|-
|Lin Wolter
|Lin Wolter
|
|Meetings on monday and thursday (3h) Expansion of problem statement (1h) More literature research (1.5h) Finding of data on electricity usage of appliances (2h)
|
|7.5
|-
|-
| rowspan="4" |4
| rowspan="4" |4
|Sven Bendermacher
|Sven Bendermacher
|
|Meetings on today and working in group (3h), Meeting Thursday (1h), learning app-loading (1h), automating data fetching for the algorithm (2h). (Due to illness I didn't do anything during the weekend)
|
|7
|-
|-
|Marijn Bikker
|Marijn Bikker
|
|Meeting on monday and working in group (3h), Writing transcript interview(0.5h), Meeting thursday(1h), learning app-coding(2,5h)
|
|7
|-
|-
|Jules van Gisteren
|Jules van Gisteren
|
|Meeting on monday and working in group (3h), Meeting thrusday (1h)
|
|4
|-
|-
|Lin Wolter
|Lin Wolter
|
|Meeting on monday and working in group (2h), Doing interview (0.5h), Meeting thursday(1h), Writing scenarios and finding needed information(3.5h)
|
|7
|-
|-
| rowspan="4" |5
| rowspan="4" |5
|Sven Bendermacher
|Sven Bendermacher
|
|Programming base layer of app (3h), Meeting on Thursday (1h), Programming setting layout (1h), Programming home page (3h).
|
|8
|-
|-
|Marijn Bikker
|Marijn Bikker
|
|Meeting on monday, programming, designing.
|
|5.5
|-
|-
|Jules van Gisteren
|Jules van Gisteren
|
|Meeting on monday, Literature study, Meeting on thrusday, Rewriting requirements, Ordering the document, Rewriting and making additions to Users
|
|8
|-
|-
|Lin Wolter
|Lin Wolter
|
|Meeting on thursday, Literature study and rewriting/ordering of state of the art. (Less meeting due to illness)
|
|7
|-
|-
| rowspan="4" |6
| rowspan="4" |6
|Sven Bendermacher
|Sven Bendermacher
|
|Meeting on monday, making the app, Meeting on Thursday, finishing the app.
|
|to much
|-
|-
|Marijn Bikker
|Marijn Bikker
|
|Meeting on monday, programming, Meeting on thursday, programming, working on the presentation
|
|8
|-
|-
|Jules van Gisteren
|Jules van Gisteren
|
|Meeting on monday, processing feedback, partitioning tasks, meeting on thursday and partitioning tasks, fixing the citations on the wiki, reworking the section headings and order
|
|7
|-
|-
|Lin Wolter
|Lin Wolter
|
|Meetings (3.5h), Programming cycle function (2h), Trying to turn python app into android app (4h)
|
|9.5
|-
|-
| rowspan="4" |7
| rowspan="4" |7
|Sven Bendermacher
|Sven Bendermacher
|
|Meeting on monday, working in group, working on presentation, working out the notifications, preparing the demo for the presentation.
|
|9
|-
|-
|Marijn Bikker
|Marijn Bikker
|
|Meeting on monday(0.5h), working in group(1.5h), literature study(1h), meeting on thursday(1h), working on presentation(1h), user research(1h), working on presenation(1.5h). Meeting on sunday(2h), Working on presentation(0.5h)
|
|10
|-
|-
|Jules van Gisteren
|Jules van Gisteren
|
|Meeting on monday, working in group, create the first version of the user tasks and interview, meeting on thursday, creating the structure of the presentation, further work out the presentation, writing text for pitch and designing the pitch Powerpoint slides, Conducting user interview, Working out user interview, preparing for the presentation
|
|10
|-
|-
|Lin Wolter
|Lin Wolter
|
|Meeting on monday, working in group, meeting on thursday, meeting on sunday, doing user test, working on presentation, literature research.
|
|9
|-
|-
| rowspan="4" |8
| rowspan="4" |8
Line 365: Line 832:
|-
|-
|Marijn Bikker
|Marijn Bikker
|
|Presenting(0.5h) , Meeting about partitioning of tasks(0.5) , User interviews and writing results(1h), rewriting introduction and problem statement(1h). Literature research(0.5h), Mail feedback.
|
|3
|-
|-
|Jules van Gisteren
|Jules van Gisteren
|
|Presenting, Attending other presentations, Meeting about the last partitioning of tasks, User test and interview results, writing the abstract, checking for spelling mistakes, fixing the citations
|
|8
|-
|-
|Lin Wolter
|Lin Wolter
|
|Meeting, Writing of wiki
|
|8
|}
|}<br />
===Appendix B: User interviews===
 
====Appendix B.1: Informed consent interviews users====
You have been asked to participate in a study for the course 0LAUK0 Project robots everywhere(2023) of Eindhoven University of Technology. This document gives you information about this study and your rights as a participant. Please read it carefully.
 
=====About the study=====
The aim of this study is to test the need for an algorithm and app using a dynamic electricity contract to steer the user towards low-use hours. The study will last approximately 20 minutes. In this study, you will be asked some questions about your opinion and preferences for such a tool. The experiment leader will make notes of what you are saying.
 
=====Voluntary=====
Your participation is completely voluntary. You can refuse to participate without giving any reasons and you can stop your participation at any time during the study. You can also withdraw your permission to use your data up to 24 hours after the study is finished. All this will have no negative consequences whatsoever.
 
=====Confidentiality=====
All research conducted at the Human-Technology Interaction Group adheres to the Code of Ethics of the NIP (Nederlands Instituut voor Psychologen – Dutch Institute for Psychologists). We will not be sharing personal information about you to anyone outside of the research team. No video recordings are made that could identify you. Only an audio recording will be made. The information that we collect from this study is used for writing scientific publications and will only be reported at group level. It will be completely anonymous and it cannot be traced back to you
 
=====Further information=====
If you want more information about this study you can ask Marijn Bikker (contact email: m.w.a.bikker@student.tue.nl). If you have any complaints about this study, please contact the supervisor, m.j.g.v.d.molengraft@tue.nl.
 
=====Certificate of Consent=====
I, (NAME)……………………………………….. have read and understood this consent form and have been given the opportunity to ask questions. I agree to voluntarily participate in this study carried out by group 1 of the course 0LAUK0 Project robots everywhere(2023).
 
Participant’s Signature: .........
 
Date: ...........
 
====Appendix B.2: Explanation before interview====
Een dynamisch contract is een vorm van energiecontract waarbij (in het geval van de Nederlandse variant) één dag van tevoren de energieprijzen voor de volgende dag worden vrijgegeven. Deze prijzen verschillen per uur, waarbij de prijzen vaak hoger liggen bij rond piek uren waar mensen over het algemeen veel stroom gebruiken - denk aan de tijd rond het avondeten -, maar waar de prijzen stukken lager - of zelfs negatief zijn! - rond de tijd waarop weinig stroom wordt gebruikt en veel groene energie wordt opgewekt.
 
<nowiki>https://www.dynamisch-tarief.nl/stroom/</nowiki> (The site doesn't allow images to be uploaded at this time, so I'm putting this in
 
====Appendix B.3: Interest interview====
 
=====Questions=====
 
=====Interview mensen met een dynamisch contract:=====
1.      Heeft u het informed consent form begrepen gelezen?
 
2.     Heeft u apparaten waar je de tijd op kunt instellen voor gebruik? De vaatwasser en wasmachine bijvoorbeeld?
 
3.     Heeft u een veelgebruiker (elektriciteit) die ‘smart’ is? Dat wil zeggen met wifi verbinding maakt en met een app te bedienen is?
 
4.     Heeft u een elektrische auto?
 
5.     Heeft u zonnepanelen?
 
6.     Wat zou u vinden van een app die automatisch apparaten aanzet op de goedkoopste momenten?  
 
7.     Wat zou u vinden van een app die elke dag 1 of 2 meldingen stuurt over wanneer de stroom het goedkoopst is?
 
8.     Heeft u een dynamisch contract, zo ja wat voor soort?
 
9.     Wat zou u belangrijk vinden aan de app, gebruikersgemak, looks of mogelijkheid tot personalisatie?
 
10.   Wat zou u vinden van een feature die bijhoud hoeveel geld er dit jaar is bespaard?
 
Beantwoord de volgende vragen van een schaal van één tot vijf, waarbij één zeer oneens is en vijf zeer mee eens.
 
11.  “Ik ben overgestapt op een dynamisch contract vanwege financiële overwegingen”
 
12.  “Ik ben overgestapt op een dynamisch contract vanwege milieu overwegingen”
 
=====Interview mensen met een vast of variabel contract:=====
1.      Heeft u het informed consent form begrepen gelezen en ingevuld?
 
2.      Heeft u apparaten waar je de tijd op kunt instellen voor gebruik? De vaatwasser en wasmachine bijvoorbeeld?
 
3.      Heeft u een veelgebruiker(elektriciteit) die ‘smart’ is? Dat wil zeggen met wifi verbinding maakt en met een app te bedienen is?
 
4.      Heeft u een elektrische auto?
 
5.      Heeft u zonnepanelen?
 
6.      Wat zou u vinden van een app die automatisch apparaten aanzet op de goedkoopste momenten?
 
7.      Zou u een app interessant vinden die elke dag 1 of 2 meldingen stuurt over wanneer de stroom het goedkoopst is?
 
9.      Wat zou u belangrijk vinden aan de app, gebruikersgemak, looks of mogelijkheid tot personalisatie?
 
10.   Wat zou u vinden van een feature die bijhoud hoeveel geld er dit jaar is bespaard?
 
Beantwoord de volgende vragen van een schaal van één tot vijf, waarbij één zeer oneens is en vijf zeer mee eens.
 
11.     “Als ik zou overstappen op een dynamisch contract, dan zou ik dit doen vanwege financiële overwegingen”
 
12.     “Als ik zou overstappen op een dynamisch contract, dan zou ik dit doen vanwege milieu overwegingen”
 
=====Transscripts=====
''Interview 1''
 
interviewer: Marijn Bikker
 
Interviewee: Floris Bikker
 
 
Interview mensen met een dynamisch contract:
 
 
1.      Heeft u het informed consent form begrepen gelezen?
 
Ja die is gelezen en begrepen.
 
 
2.     Heeft u apparaten waar je de tijd op kunt instellen voor gebruik? De vaatwasser en wasmachine bijvoorbeeld?
 
Ja die hebben we.
 
3.     Heeft u een “veelgebruiker“ (elektriciteit) die ‘smart’ is? Dat wil zeggen met wifi verbinding maakt en met een app te bedienen is?
 
Nee dat hebben we niet.
 
4.     Heeft u een elektrische auto?
 
Nee
 
5.     Heeft u zonnepanelen?
 
Nee
 
6.     Wat zou u vinden van een app die automatisch apparaten aanzet op de goedkoopste momenten?
 
Handig, maar het zou een probleem kunnen zijn op de momenten wanneer de apparaten niet klaar zijn voor gebruik. En bovendien als je al bezig bent met de apparaten, dan is het nog maar een kleine moeite om zelf ook de tijd in te stellen wanneer hij moet draaien.
 
Voor elektrische auto echt handig.
 
Nu is het zelf in de energie-app kijken een klein beetje gedoe en gereken, een app die dat zelf doet zou misschien makkelijk zijn. Nu is het nog nieuw en leuk om in die app het even op te zoeken en uit te rekenen, misschien dat dat over een tijd minder leuk om te doen is.
 
7.     Wat zou u vinden van een app die elke dag 1 of 2 meldingen stuurt over wanneer de stroom het goedkoopst is?
 
Best handig.
 
8.     Heeft u een dynamisch contract, zo ja wat voor soort?
 
Ja, per uur variërende prijs.
 
9.     Waarom bent u overgestapt op een dynamisch contract, voor besparing van geld, het milieu of een andere reden?
 
Beiden wel. Geld en milieu. Tijdens piek uren wordt er natuurlijk elektriciteit opgewekt met fossiele brandstoffen, als wij dan elektriciteit kunnen gebruiken tijdens uren waarop er meer groene stroom is dan scheelt dat voor het milieu.
 
10.  Wat zou u belangrijk vinden aan de app, gebruikersgemak, looks of mogelijkheid tot personalisatie?
 
Gebruikersgemak het belangrijkst. Hoe de app eruit ziet niet zo.
 
11.  Wat zou u vinden van een feature die bijhoud hoeveel geld er dit jaar is bespaard?
 
Ja leuk.
 
===Research on profit of dynamic contract===
To get costumers to switch to a dynamic contract, it is important to study the possible profit of switching to such a contract.
 
We look at a period of at least a year, since the price one pays in the dynamic case differs a lot in the summer and winter because of the difference in electricity consumption. To compare a fixed contract with a dynamic contract, some assumptions have to be made. The most important assumption is that the market price of the electricity stays more or less the same throughout the years. The market price can change due to global (in)stability or big events, such as wars or pandemics affecting the financial market. For our comparison we assume we are dealing with a stable market price.
 
The business works as follows. The energy suppliers sell the electricity to the customers with a fixed price, whilst they buy the electricity on the market for fluctuating prices. In general the price the costumer pays is higher than the price the energy suppliers pay, since the energy suppliers have to make profit. But for the fixed or variable contracts the energy suppliers work with an additional safety margin for the case of rising prices. In the case of the dynamic contract there is only the small addition in price for the energy suppliers to make profit.
 
Then there is a risk involved in switching to a dynamic contract. You have to pay more when the market prices increase, whereas someone with a fixed contract is not affected by this, until the moment his contract expires and he has to sign a new contract.
 
The costumer with the dynamic contract can thus profit from the moments the market price is lower than the price the fixed contracts offer. To profit from this, the costumer must be active and use these moments to use electric devices or load their laptops and phones. The study of the European commission gives insight into the possible costumers that use a dynamic contract profitably.
 
===Old interview that wasn't conducted: Usability requirements interview===
Two requirements of our product are based on the experience of the user of the product:
 
*The user should not feel disrupted by the app;
*The environment in which the user interacts with the product should be easy to navigate, such that users with every level of experience with technology should be able to know what they are telling the app and what the app is telling them.
 
Because we cannot easily check if our product contains these requirements - we as creators might not feel disrupted by the product and we already have a good idea of how the app works Because we designed it - we are going to check for these requirements using an interview. Since we already want to gain some insight into this requirement before we finished a working version of the product, we are going to conduct this interview on the hypothetical use of this product. Before this interview, the interviewees will have signed the informed consent form and have been told some preliminary knowledge about the state-of-the-art of our product, which can be found in Appendix B.1 and Appendix B.2 respectively.
 
The following questions should be answered using the Likert scale (i.e. choosing between strongly disagree, disagree, neutral, agree or strongly agree). The questions are - as of now - written in Dutch. In the future we will include both an English and Dutch version.
 
#Een app die mij verteld wanneer het beste moment zou zijn om geld te besparen met een dynamisch contract zou mij eerder over laten stappen tot een dynamisch contract.
#Ik heb liever dan een algoritme checkt wat het beste moment is om mijn apparaten aan te zetten dan dat ik dat zelf zou doen.
#Een app die op een vast moment op de dag een notificatie stuurt op mijn telefoon wanneer ik het beste mijn apparaten (die stroom gebruiken) (vaatwasser, wasmachine, droger, etc.) aan kan zetten om geld te besparen zou ik prettig vinden.
#Een app die mij op een scherm in een app kan laten zien wanneer ik het beste mijn apparaten aan kan zetten zou ik prettig vinden.
#Ik zou liever een notificatie krijgen die mij herrinert wanneer ik mijn apparaten aan moet zetten dan dat ik zelf op een scherm moet checken.
#Ik zou een notificatie op een door mij zelf ingesteld op de dag kunnen instellen zodat deze altijd
 
====Appendix B.4 User test and interview====
.............
 
=====Interview Frank Bendermacher=====
1.     '''How would you feel about a notification that would remind you to check for the best time to turn on your devices?'''
 
Als ik de informatie niet te lang hoef te onthouden is dat handig.
 
 
2.     '''How would you feel about a notification for each device at the time that it should be turned on?'''
 
Dat is handig zodat ik daar zelf niet mee bezig hoef te zijn, dat ik nog op een bepaalt tijdstip een apparaat moet aanzetten.
 
 
3.     '''Would a notification reminding you to turn on a device always be effective for you?'''
 
Als de melding altijd op het juiste moment komt en alleen als het van toepassing is zou het zeker effectief zijn.
 
 
4.     '''Would a price indication alongside the notification, showing you how much money you would save if you would follow the notifications instructions and turn on a device, increase the effectiveness of the notification for you?'''
 
Over het algemeen denk ik dat het weinig invloed heeft of ik iets met de notificatie doe.
 
 
5.     '''Would the notifications be more effective for you when they would always arrive when you are already running chores?'''
 
Ja dan hoef ik de tijd niet lang te onthouden waar ik het apparaat op moet instellen.
 
 
6.     '''What else could increase the effectiveness of notifications for you?'''
 
Als het informatie geeft waar ik iets aan heb, op de juiste momenten.
 
 
7.     '''Would you experience disruptiveness by the number of notification per day (which is equal to the number of devices listed plus one general notification)?'''
 
Zoals eerder aangegeven, zolang de meldingen goede informatie geven op de juiste momenten zijn de meldingen niet storend, en ervan uitgaand dat ik 3 tot 4 apparaten in de app heb zou gemiddeld 3 meldingen per dag niet storend zijn.
 
 
8.     '''What number of notifications would it take for you to experience disruptiveness?'''
 
Het gaat niet zozeer over het aantal maar meer over dat ik niet twee keer dezelfde melding wil krijgen op een dag, en ik wil ook niet te veel meldingen krijgen waar ik niks aan heb.
 
 
9.     '''Would a price indication alongside the notification decrease the level of disruptiveness?'''
 
Nee, als de notificatie goede informatie geef over het apparaat waar ik mee bezig ben dan geeft die prijs indicatie geen meerwaarde, want als ik het apparaat ben aan het instellen dan wil ik gewoon de tijd weten wanneer de prijs het laagste is, en de desbetreffende prijs bij dat minima is dan niet een factor.
 
 
10.  '''Would the level of disruptiveness due to the notifications be decreased if the notifications would always arrive when you are already doing chores?'''
 
Ja zeker, zoals eerder aangegeven helpt dat met de effectiviteit van de notificatie.
 
 
11.  '''What else could decrease the level of disruptiveness due to the notifications for you?'''
 
Zoals het voorbeeld met de live activity notificatie dat ik nog dingen kan veranderen in de melding zonder de app te openen maakt de melding effectiever en dan dus minder storend. En ik hoef uiteraard geen melding te krijgen dat ik een apparaat moet aanzetten als ik dat op het apparaat zelf al heb ingesteld.                 
 
=====Interview Nicole Wolter=====
 
======Tasks======
1. Check what time the hourly energy costs are highest;
 
Easy to see, done quickly.
 
2. Check at what time you should activate the dishwasher today;
 
Bit more effort but doable. Do the home screen times even work?
 
3. Add an electric car to the devices list;
 
Easy to do, the information text helps.  
 
4. Check at what time you should charge the electric car today;
 
Doable, easy to see.  
 
5. Check how much energy is used by running the dishwasher one time;
 
Easy to see.
 
6. Check how much money you have saved by charging the electric car on time.
 
Easy to see.  
 
<br />
 
======Questions======
1.     '''How would you feel about a notification that would remind you to check for the best time to turn on your devices?'''
 
Nicole wolter: Easy. Nice to get reminded
 
 
2.     '''How would you feel about a notification for each device at the time that it should be turned on?'''
 
Nicole: Every device should be separate. To get the most optimised result
 
 
3.     '''Would a notification reminding you to turn on a device always be effective for you?'''
 
Nicole: Yes I have to be reminded of course.
 
 
4.     '''Would a price indication alongside the notification, showing you how much money you would save if you would follow the notifications instructions and turn on a device, increase the effectiveness of the notification for you?'''
 
Nicole: In theory it would help but if the profits are too small it may lead to less incentive.
 
 
5.     '''Would the notifications be more effective for you when they would always arrive when you are already running chores?'''
 
Nicole: A fixed time can also be nice but separate messages at best times is also good.
 
 
6.     '''What else could increase the effectiveness of notifications for you?'''
 
Nicole: I don’t know.
 
 
7.     '''Would you experience disruptiveness by the number of notification per day (which is equal to the number of devices listed plus one general notification)?'''
 
Nicole: I wouldn’t find this annoying.
 
 
8.     '''What number of notifications would it take for you to experience disruptiveness?'''
 
Nicole: Around 10 would be too much.
 
 
9.     '''Would a price indication alongside the notification decrease the level of disruptiveness?'''
 
Nicole: Already answered on this, does not effect.
 
 
10.  '''Would the level of disruptiveness due to the notifications be decreased if the notifications would always arrive when you are already doing chores?'''
 
Nicole: Would be less disruptive. Nice to set your own time for the notifications then.
 
 
11.  '''What else could decrease the level of disruptiveness due to the notifications for you?'''
 
Nicole: I Don’t know.
 
<br />
 
=====Interview Hans van Gisteren=====
 
======Tasks======
1. Check what time the hourly energy costs are highest.
 
A1: Gelukt; Meteen gezien.
 
2. Chek at what time you should activate the dishwasher today.
 
A2: Niet gelukt; Ging er van uit dat de “start time” en “end time” de momenten waren waartussen je hem het beste aan koon zetten.
 
3. Add an electric car to the devices list.
 
A3: Bijna helemaal gelukt; Niet volledig duidelijk dat het icoontje ook opslaan betekende; Stelde voor om een tekst naast het icoontje te zetten.
 
4. Check at what time you should charge the electric car today.
 
A4: Niet gelukt; Hetzelfde probleem als in taak 2. gebeurde opnieuw.
 
5. Check how much energy is used by running the dishwasher one time.
 
A5: Gelukt; meteen gezien.
 
6. Check how much money you have saved by charging the electric car on time.
 
A6: Gelukt; meteen gezien.
 
 
Na uitleg waren taak 2. en 4. ook gelukt. Wel had gaf hij bij opdracht 4. De suggestie om i.p.v. “75%. 50% 25%” ook 100% toe te voegen bij de optie voor het opladen van een elektrische auto: “Kan je hem niet helemaal opladen? Dat is wel een beetje suf toch?. Dan zou ik, bijvoorbeeld, 100%, 50% en 25% doen.”
 
======Questions======
1.     '''How would you feel about a notification that would remind you to check for the best time to turn on your devices?'''
 
Hans van Gisteren: Lijkt me handig.
 
 
2.     '''How would you feel about a notification for each device at the time that it should be turned on?'''
 
Hans: Lijkt me prima, maar 1 druk op de knop.
 
Jules van Gisteren: Dan moet je wel de vaatwasser aanzetten;
 
Hans: Dan moet je wel thuis zijn om dat te doen dus?
 
Jules: Dat wel, maar je kan hem van tevoren aanzetten.
 
Hans: Dat doe ik bv bij de wasmachine wel al, om kreukelen te voorkomen bv
 
Jules: Met smart devices zou dit allemaal geautomatiseerd worden
 
Hans: Daar zal het ook wel naar toe gaan uiteindelijk allemaal. Ook al kan ik bij de vaatwasser nog geen tijd instellen.
 
 
3.     '''Would a notification reminding you to turn on a device always be effective for you?'''
 
Hans: Jawel
 
 
4.     '''Would a price indication alongside the notification, showing you how much money you would save if you would follow the notifications instructions and turn on a device, increase the effectiveness of the notification for you?'''
 
Hans: Ja dat zou me wel extra motiveren. Ik zou het sowieso al doen, maar dat me nog wel extra motiveren; Dat is toch wel een extra drive.
 
 
5.     '''Would the notifications be more effective for you when they would always arrive when you are already running chores?'''
 
Hans: Dan moet ie wel weten dat je thuis bent.
 
Jules: Stel hij zou dat weten, zou het dan effectiever zijn?
 
Hans: Dan zou het opzich wel kunnen helpen
 
 
6.     '''What else could increase the effectiveness of notifications for you?'''
 
Hans: Dat je een functie hebt om te melding aan te vinken zodra je de taak gedaan hebt.
 
 
7.     '''Would you experience disruptiveness by the number of notification per day (which is equal to the number of devices listed plus one general notification)?'''
 
Hans: Er komen al zo veel appjes binnen, dus dat kan er ook wel bij. En als je het een keer mist is het ook zo’n drama.
 
 
8.     '''What number of notifications would it take for you to experience disruptiveness?'''
 
Hans: Boven een stuk of tien.
 
 
9.     '''Would a price indication alongside the notification decrease the level of disruptiveness?'''
 
Hans: Als het bijna niks scheelt zou het alsnog irritant kunnen zijn als het te veel appjes zijn, maar als het dan qua prijs flink scheelt dan mogen het ook meer appjes zijn.
 
 
10.  '''Would the level of disruptiveness due to the notifications be decreased if the notifications would always arrive when you are already doing chores?'''
 
Hans: Ja, ik kan anders toch niks doen op dat moment. Dan krijg ik ze het liefst ook allemaal binnen als ik thuis wat kan doen.
 
 
11.  '''What else could decrease the level of disruptiveness due to the notifications for you?'''
 
Hans: Dat ze niet telkens terugkomen als je de taak hebt afgerond. Dus dat je hem kan afvinken zodra je hem klaar hebt en de melding voor dat apparaat daarna stopt.
 
Jules: Maar de melding moet dus wel terugkomen als de taak nog niet is voltooid?
 
Hans: Ja dan wel?
 
Jules: Dan is het niet vervelend?
 
Hans: Nee dan niet. Misschien zou je er dan een maximum bij kunnen zetten, zodat als je de melding bijvoorbeeld al vijf keer hebt gezien dat je hem dan niet nog een keer krijgt, want dan ben je het toch niet van plan.
<br />
 
<br />
 
=====Interview Floris Bikker=====
 
======Questions======
1.    How would you feel about a notification that would remind you to check for the best time to turn on your devices?
 
Good idea
 
2.    How would you feel about a notification for each device at the time that it should be turned on?
 
Good idea, more effective then 1.
 
3.    Would a notification reminding you to turn on a device always be effective for you?
 
Depends on whether you are home. If I am home effective. But not in the night
 
4.    Would a price indication alongside the notification, showing you how much money you would save if you would follow the notifications instructions and turn on a device, increase the effectiveness of the notification for you?
 
Yes a little bit
 
5.    Would the notifications be more effective for you when they would always arrive when you are already running chores?
 
Effective, but I already do that right now with the ANWB app.
 
6.    What else could increase the effectiveness of notifications for you?
 
Notification if the washing machine is ready.
 
7.    Would you experience disruptiveness by the number of notification per day (which is equal to the number of devices listed plus one general notification)?
 
No 4 or 5 notifications is fine. But maybe I would like to adjust this for devices, since not every device is used everyday.
 
8.    What number of notifications would it take for you to experience disruptiveness?
 
Difficult question. That is rather something you have to experience.
 
9.    Would a price indication alongside the notification decrease the level of disruptiveness?
 
No it makes the notification more effective.
 
10. Would the level of disruptiveness due to the notifications be decreased if the notifications would always arrive when you are already doing chores?
 
No that is more effective.
 
11. What else could decrease the level of disruptiveness due to the notifications for you?
 
I cant think of anything right now.
 
 
=====Interview Astrid Fintelman=====
 
======Tasks======
The general feedback is that the app was user-friendly. Every tasks was completed successfully. However, some feedback for possible improvements is:
 
- Right now it looks like the timeframes can be adjusted on the 2nd screen, whether they can only be adjusted in the setting. This is confusing. The idea behind this design is however an implemented version on your phone, and on your phone you can indeed adjust the timeframe on the second screen
 
- It took some time to figure out how to change the starting time of the timeframe, since the button of start at current time in the settings was overlooked. After this button was found the problem was resolved
 
- The text activate is a little bit confusing, since it only means in the current version of the app that a cycle of the device is done. Other possible names could be: "used" or "operated".
 
<br />
 
======Questions======
1.    How would you feel about a notification that would remind you to check for the best time to turn on your devices?
 
Sounds great.
 
2.    How would you feel about a notification for each device at the time that it should be turned on?
 
Sounds great as well.
 
3.    Would a notification reminding you to turn on a device always be effective for you?
 
I would also ignore them from time to time.
 
4.    Would a price indication alongside the notification, showing you how much money you would save if you would follow the notifications instructions and turn on a device, increase the effectiveness of the notification for you?
 
No, that would be interesting after a month, but not always per cycle. Not necessary.
 
5.    Would the notifications be more effective for you when they would always arrive when you are already running chores?
 
Maybe that would result in too much notifications. For example for the dishwasher, you would open that one many times a day, and if it sends a notification every time you open it It would be disruptive.
 
6.    What else could increase the effectiveness of notifications for you?
 
What would motivate me is a notification each month or each week how much money you saved.
 
7.    Would you experience disruptiveness by the number of notification per day (which is equal to the number of devices listed plus one general notification)?
 
Yes that would be a bit much for me.
 
8.    What number of notifications would it take for you to experience disruptiveness?
 
5 already. 3 is fine.
 
9.    Would a price indication alongside the notification decrease the level of disruptiveness?
 
No since I would not be interested in those small savings.
 
10. Would the level of disruptiveness due to the notifications be decreased if the notifications would always arrive when you are already doing chores?
 
No
 
11. What else could decrease the level of disruptiveness due to the notifications for you?


==Literature==
If you can set the settings of the timing of the messages yourself. So you will not get a message to early in the morning.
<references />
==Bibliography==
<references /><br />

Latest revision as of 22:58, 30 October 2023

Group members
Name Student number Major
Sven Bendermacher 1726803 BAP
Marijn Bikker 1378392 BAP
Jules van Gisteren 1635530 BAP
Lin Wolter 1726927 BAP

Abstract

This paper focusses on the research and the development of an app that increases the profit from a dynamic electricity contract and decreased the effort needed from the consumer with respect to the current dynamic electricity apps. From literature research and user experience with the app it was found that the app would help consumers use a dynamic electricity contract more efficiently and would help with the implementation of green energy. The state of the art is discussed, the deliverables and the users are listed. Then the design of the app, the algorithm behind the app and the challenges faced during the development are discussed. After this the result of the user study is given, followed by a conlclusion and discussion with future recommedations.

Introduction

Since the emergence of more renewable energy and of more energy intensive technologies, like air conditioning needed due to rising temperatures and more extreme weather, the electricity landscape has changed. The increase in load on the electricity net has to be counteracted by either improvements of the network or measures to decrease the peak load on the net. Next to this consumers often pay high prices for electricity due to producers having to power on fossil fuel plants to reach the electricity production needed for the peak times of the day, while sometimes renewable electricity is lost due to lack of use during high sun low use times. To fix this problem producers and governments have turned to introducing dynamic electricity contracts, where consumers pay different amounts of money depending on the time of consumption, thus making it more financially wise to use electricity on the low peak times. This usage of the electricity on different times can even lead to profits on the end of the consumer, and helps the planet and producers to increase the share of renewable sources of electricity.

Problem statement

The dynamic electricity contract is now done manually by the consumer. The consumer takes a graph of the electricity prices per hour and based on that plans the use of their flexible electric devices. However, the consumer can only optimize this to a certain extent. An algorithm that knows the exact cycles of electric devices can calculate with more precision the most favourable time for the electric devices.


An algorithm should be developed that has the data from all electric devices regarding the electricity use as a function of time for each cycle. As an illustration, the cycle dishwasher uses more electricity in the beginning to warm up the water then at the end of the cycle. The algorithm should then calculate the total price of the cycle based on the hourly electricity cost graph with a correlation function. The algorithm then needs as an input the timeframe the electric devices have to operate in. The resulting starting times should be displayed in an application. The application has to be easily accessible for users, this could be either done with a website or an app, the app will probably lead to the best possible integration since messages that have to be send would in case of the website require the phone number of the user. The app would then also be connected to the other systems the user has such as their electricity providers app, the app of smart appliances and app of the electric car. With all this the system, most likely in app form, would then become a hub for all the users electricity based needs. Where the system even optimizes dynamic electricity pricing to be as profitable as possible, in turn helping the electricity network by de-loading the network at peak times enabling the better inclusion of renewable forms of electricity. Another important factor to take into account for the system is that solar panel users need other advice than people without solar panels, since the solar panels can sometimes cost money, due to negative prices, or can save much money due to times of high solar intensity. The negative prices are caused by over congestion on the electricity grid, which if not handled would lead to failure of the grid, this is especially caused by the large amount of renewable, unstable sources of electricity. Even the minister of energy of the Netherlands states that it is sometimes more beneficial for consumers with solar panels to turn them off in times of high negative prices, this is something that the system could also control or at least mention to the consumer. In times of no negative prices the use of solar electricity is always free, thus the system must take this into account. The price of electricity might be lowest in the morning but when the solar intensity is higher in the afternoon it would be more cost beneficial to turn on the appliances during times that the consumers' solar panels are peaking in electricity generation.


The dynamic electricity contract needs, at the moment, still a large amount of effort and input from the consumer to achieve the promised savings on electricity bills. Which thus leads to less people switching over to a dynamic electricity contract hampering the decrease of load on the net and inclusion of renewable electricity sources.

A system is thus needed to decrease the effort needed to be done by the consumer, such a system would in the best case automate all appliances based on the before set operation times and the known electricity prices. This automation would need a house full of smart appliances, which are connected to the internet and can thus be turned on remotely. Research done into these systems has shown that consumers need direct messages in order to be reminded of the time to turn on the appliances, which then leads to a efficient use of the dynamic electricity contract, but this research has also shown that consumers would rather have everything automated. This automation however is still very hard due to the lack of smart appliances in most households. Research also shows that consumers benefit from an overview of their electricity usage. The system to solve this problem would thus need at minimum an overview of electricity usage, a time window input, to determine the times the consumer would accept and be able to turn on the appliances, a message that is send at the prime-time the consumer has to turn on the appliance which could include the savings achieved by the to be undertaken action and in best case a direct link to the smart appliances to automate the activation of the appliances. Another situation that is interesting for our product is consumers in possession of an electric car. They could use their electric cars as electricity storage, for this to work however many things have to be kept in mind. The electric car has to still be charged for the travel to be done by the user, thus another input is needed where the minimum charge can be set by the user, this in turn then enables the system to better optimize electricity use since the car could be charged at times of low prices and then discharged at peak times thus leading to even more savings for the user. The health of the car battery however has also to be taken into account, since the savings on electricity bills would need to counteract the degradation of the batteries well enough to be worth it. This is dependent on the age of the car as well as the type of car. The system would thus need much more info and should in the best case be directly connected to the software of the electric car.

State-of-the-art

To get a firm grasp of our subject we conducted literature research by reading numerous articles related to the subject. We documented our research below in the form of a short summary of each article respectively.

Research on consumer risks and benefits of dynamic electricity price contracts[1]

This research states that there is serious risk involved in switching from fixed prices to dynamic prices. It concludes that there is only little room for flexible electric consumption, and that the average dynamic prices in France and Austria were higher in 2021 than the fixed price. Furthermore it is said that for households with an electric vehicle(EV) a dynamic electricity bill could be beneficial, since a EV is the biggest consumer within the flexible electricity consumption activities. However, in this paper it is stated that most of the electric consumption is used for space heating and water heating. In the Netherlands we use natural gas to warm up our homes, so the situation might be different and more profitable for Dutch households.

Asset Study on Dynamic retail electricity prices[2]

This research says that consumers can significantly decrease their electricity bill by shifting to low price moments. It evaluates different kinds of dynamic pricing options, Real time pricing, time of use up till critical peak pricing. Real time pricing and time of use pricing are the riskiest but yield the highest reward, the critical peak pricing has the lowest risk but yield a lower reward. This research also states that a dynamic pricing leads to a more efficient electric grid, since lower peak demand reduces the losses in the electricity grid. This also results in a lower electricity bill.  Additionally, dynamic prices incentivise demand shifting to times of lower prices which usually indicate times of high intermittent renewable energy resources (RES) feed-in. The use of excess electricity can reduce local congestion and therefore facilitate the integration of RES in the energy system. Therefore it would also be in the interest of the government to promote switching to dynamic prices.

Furthermore it gives an overview of the potential customers for dynamic pricing. With a premium on the electricity prices reducing the maximum prices, up to 90 percent of the costumers could profit from dynamic pricing.

Dynamic electricity pricing — Which programs do consumers prefer?[3]

TOU vs RTP, RTP is real time pricing where the user pays based on the real time market prices which change every hour. TOU is time of use pricing where the price is fixed long in advance on a timetable.  

Consumers are fine with using dynamic electricity pricing as long as their daily routine is not affected by it or lead to reductions in their comfort level. When asked about electricity contracts people seem to still prefer a static contract. People tested in a dynamic pricing situation proposed multiple insights. Things like lights, tv, stove were things where the price at that point was not really taken into consideration, since the people thought it would affect their lifestyle too much. Things like dishwashers, washing machines and tumble dryers were used much more at low price times. Due to work however the participants of the test could not always benefit from the low prices and seemed to be less willing to turn on the appliances very early or very late in the day. People also preferred RTP over TOU since RTP made the users feel that they could save more money.  

Via a questionnaire it was found out that most people prefer a constant rate even though the advantages of a dynamic contract were explained thoroughly. The cost-saving expectation was 50 – 150 euro but turned out to only be 20 to 60 euro. Therefore, it is necessary that the other advantage of dynamic pricing, the load shifting, to be explained thoroughly. And the participants expressed a wish for demand automation, thus turning on the appliances at low price times automatically in order to make the dynamic pricing seem worthwhile.

Influencing residential electricity consumption with tailored messages: long-term usage patterns and effects on user experience[4]

Persuasive technologies are an important method to alter consumer behaviour next to financial benefits. Personalized persuasive technologies work better to stimulate people into doing what is wanted of them. Things like tailored information, personalized content, cooperation and competition are known to be good design principles. Messages to the user should be send at appropriate times and should be managed to not create irritation.  

In a trial done with real household many insights were found by the authors. The program where users could gain more insights next to only SMS messages stating the best time to turn on the appliances was used quite little by the users, the users that did at first often use it proceeded to use it less as the trial went on. The users were happy with being able to see achieved savings as well as being able to see a comparison between real and optimal consumption curves. Users say that an in-home display could have stimulated them even more. Also, next to time and possible savings the rate should also be included in the message sent to the users. It was seen that the washing machines and dryers were shifted most often to accompany electricity savings, while dishwashers the least. Users again brought up a preference for automation. Users finally also mentioned that while they were willing to alter their behaviour it was often hard to do this due to work or other unavailability.  

The authors state that their personalized approach did not lead to a higher willingness to use the program than other untailored approaches used in other experiments. The schedules of the users could be taken into account to better approach users for effective savings. If the savings are only very small other incentives should be possibly used such as a widespread reduction in CO2 emissions.

Integration of electric vehicles in smart grid: A review on vehicle to grid technologies and optimization techniques[5]

Bidirectional V2G, vehicle to grid, has many advantages such as reduce power grid losses, prevent power grid overloading, minimize emissions, maximize profits and help intermittent renewable energy. The drawbacks however are battery degradation, the need for more complex hardware, a high investment cost and social barriers, because people want their car to be charged in case of emergency. Next to cost advantages the vehicle to grid also gives the owners backup power in case of a blackout. The article states that due to battery degradation V2G has to either be completely avoided or correctly optimized in order to only discharge the batteries slowly and till at max 60 percent of full capacity. This maximal percentage also works well taking into account social barriers where car owners want a certain battery level at all times. Finally, the authors state that an incentive-based system is needed for V2G to be taken up by many electric vehicle owners.  

The effects of household automation and dynamic electricity pricing on consumers and suppliers[6]

The article states that the amount of savings done by household automation depends on the household's energy consumption and production through the day. It also depends on the size of the household how much savings can be done and if they are done at all. The presence of solar panels can in fact lead to less profits for a single person household. The automation of households leads to savings in both TOU and RTP pricings, with more savings with RTP. Especially profits made with solar panels can be a great incentive for the households. The suppliers however have a decrease in profits due to the increase in solar panels and automation of the households. Thus, suppliers should tailor their contracts to the consumers if the suppliers want to maximize their own profits.  

Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark[7]

This paper sets out to find the state-of-the-art electricity price forecasting models, it describes problems that make comparing of different models hard and also state that there is no clear benchmark to check the performance of models to. The paper states that there are three main models, statistical models, machine learning models and hybrid models. The comparison of these 3 is very hard thus leading to the authors stating not one single state-of-the-art method but choosing multiple. For the statistical models the authors decide that the LEAR model is very accurate, while for the machine learning models the DNN model is most state-of-the-art. The hybrid models they state to be not compared enough to other models thus they decide to leave them out of consideration.  

LEAR stands for Lasso Estimated Auto Regressive, where Lasso is a regression analysis method that performs both variable selection as well as regularization, which is useful to increase the quality of the dataset used for the model. Auto regressive just points to the type of model being based on time series analysis.  

DNN stand for Deep Neural Network, which is a type of machine learning with the objective of trying to replicate the way a human brain thinks. The deep part stands for it being multiple levels of machine learning. These models can be better in analysis and prediction than the statistical models but do use much more computing power.  

Residential Demand Response Based on Dynamic Electricity Pricing: Theory and Practice [8]

This paper is very long but gives a few good insights into dynamic pricing, it shows that dynamic pricing does indeed reduce the load of mid-peak and peak power plants over the year, while also adding that the peak power plants have to indeed be turned on less due to dynamic pricing. The dynamic pricing also apparently works best when there is already a large amount of renewable and uncontrollable sources of energy.  

Further it states that correct meters are needed in household in order to make sure that the electricity need is measured correctly for every hour such that the dynamic pricing can work well. The addition of online monitoring, graphic user-interfaces and in-house displays can help provide useful information to make dynamic pricing work more efficient. The automation is also a wish of households such that dynamic pricing is correctly triggered, and it does not affect the comfort of people, it also makes the use of dynamic pricing more reliable leading to a better integration of the demand spreading. Finally, the author states that substantial savings can be made but this depends on if an electric vehicle is involved and other factors. A balance has to be found between prices and practicality.

Demand side management of industrial electricity consumption: Promoting the use of renewable energy through real-time pricing[9]

This paper looks at the effectiveness of demand side management of electricity usage, which is thus using real time pricing, but in the case of large industrial electricity consumers. The industrial consumer consisting of a highly efficient cold storage were able to decrease their electricity costs dramatically by looking at the day ahead pricing of the electricity. This thus led to an increase in the amount of wind energy that was used since this corresponded to the electricity prices. Another industrial consumer, a manufacturing plant, showed to not benefit from real time pricing due to a very inflexible load profile, thus leading to much electricity use at high load times.  

The study/paper found a clear relation between the adaptation of consumers to the dynamic pricing and an increase in the use of renewable wind energy, thus showing that an increase of users of dynamic pricing would often increase the consummation of all the renewable energy. The dynamic pricing must however give a financial gain otherwise the consumer is not rewarded enough for the effort needed to follow the electricity demands.

Electric vehicle charging and discharging scheduling strategy based on dynamic electricity price[10]

The paper set out to make a deep learning algorithm capable of most optimally charging an electric vehicle depending on the dynamic electricity prices. It states that the use of this algorithm more evenly spreads the charging of the vehicles to low-peak times while still having the requirement for the vehicles to always be fully charged. The use of the algorithm led to more robustness in the electricity usage since the addition of more vehicles would not lead to a huge increase in peak loads. The algorithm used also led to big savings for the users saving them much money on the charging of their electric vehicles, while also making more use of renewable sources of electricity. The authors state that the algorithm also takes into account the flexibility of charging and discharging. The paper however does not take into account the degradation of the batteries of the electric vehicles, this should be taken into account since this contributes much to the acceptability of the algorithm. In total though the algorithm used would lead to better spread of electricity usage, better integration of renewable energy and would reduce power grip consumption.

Report of the Quantitative Field Experiment Analysis[11]

The main findings found in the paper on the experiment run with households using an informative app on electricity usage and prices, sending a part of the households a message when the price was lowest, and savings could be most achieved. The research found that a discount message led to an increase in electricity usage of around 1 percent on weekdays and 0.8-2 percent on weekends, where messages stating environmental reasons may elicit more wanted behaviour. Households which used the app heavily had even higher savings of up to 7 percent.  

Peer-to-peer comparison shown in the app led, according to the paper, to a decrease in electricity usage for many households. A gaming functionality which was also added also seemed to contribute to more savings for the households and thus more usage of the available renewable energy. The test also showed that users start out using the app more and after time learn and thus have to use the app less.  

The research state that an app needs: a dynamic tariff scheme, frequent and easily accessible information on marginal prices and frequent and easily accessible information on the household's electricity consumption. Finally, they state that the consideration of ‘smart’ appliances should be taken into account since automation can play an important role in the future.  

Analyzing the impact of dynamic electricity prices on the Austrian energy system[12]

The paper has looked into the extent that an app has effect on making a large group of test households adapt to dynamic electricity pricing. The authors looked at how flexibility of the electricity demand can have an effect on electricity usage in both a case with the use of an app stimulating correct behaviour and without such an app. The use of more elastic electricity usage led to a decrease in total system costs and led to an increase in the renewable energy usage. Although comparing to a fully renewable energy focused setup the elasticity of the groups led to a bit more carbon emissions. The use of the app led to an increase of 0.6 percent in total reduction of the system costs, going from 2 percent to 2.6 percent. The elasticity in the electricity use could in some cases lead to more peaks and less smoothness, these peaks however are not on prime times and would not lead to a large need for more fossil fuel-based electricity sources.  

My Phone and Me: Understanding People’s Receptivity to Mobile Notifications[13]

A paper from 2021 reporting on a quantitative research using a mobile phone app researches the behaviour of mobile phone users when interacting with notifications. A. Mehrota et al. constructed a mobile app that monitors mobile phone notifications, the way the users interact with these notifications and why they chose to act like they did. They app measured the following: The time it took for the notification to be seen by the user, or the "seen time"; The time between the user seeing the notification and acting on, or the "decision time". The following was asked in a survey, which the users would sporadically receive and fill in: The way the users would respond to these notifications; The reason why they acted like they did; The complexity of the task they were performing while receiving a message; The effect the notification had on the user; The general personality traits.

The paper found several connections between these measurements and/or surveys, such that it would be too much to mention all of them in this summary. Because of that, we will be listing some connection that were found in this paper which are useful for our own project: Users would see a notification quicker when performing a more complex task: Users would experience more disruptiveness from a notification when performing a more complex task; Users would open the notification, despite performing a complex task, if the notification contained useful information; Users would experience more disruptiveness when receiving a notification when they almost finished a task.

Most important remarks in paper on the preferences for dynamic electricity tariffs[14]

Stating the most important remarks from the article it is found that, consumers want on average a 12.22 percent reduction in costs to switch from a fixed tariff to a dynamic tariff, where males need an extra 10 percent. People who stated it to be easy to spread load to low load moments were more likely to switch to a dynamic contract, which of course makes sense. Next to ease of use the environmental considerations also led to quite a large percentage of people being willing to switch to dynamic tariff.  

Thus, indeed taking environmental and system benefits into account, where system benefits are in the form of load reduction on the net, led to people being more willing to switch from a fixed contract to a dynamic contract. This environmentally conscious group of people were 10% more likely to switch. Next to environmental considerations the ease of use was also very important showing a need for automation of load usage.  

Important findings in paper on household energy rules and activities during peak demand[15]

This article used a survey to inquire into people's willingness to shift their load usage to other times for different electricity using activities in the house. During peak times the activities most households took part in were watching TV, cooking with an oven/stovetop, using a computer/laptop/game console and using lights. From the people who answered that they did these activities, which ranged from 80-90 percent of respondents only around 20 percent of them state to be willing to shift these activities. Activities only 40-50 percent of people said they did during peak times were showering and running the dishwasher, clothes dryer and washing machine. For these activities however almost all respondents were willing to shift the time of use to another time where the load is less, except for showers where only half of the peak time showering correspondents were willing to shift. Finally, the survey showed that one third of electric vehicle owners charged their vehicles during peak times, of these again almost all were willing to shift their time of load.  

Another founding made was that households with more smart devices were more willing to shift their electricity usage, pointing at a possible link between more smart devices and an increase in interest in the electricity usage of these people. This however did according to the authors not have a link to automation associated with smart devices since this, according to them, still remains an open question.

Summary of paper on the costs and benefits of real-time pricing[16]

This paper looks into the possible savings which can be made by switching to a dynamic contract, where they use smart meter data from homes in the USA. The paper states that even if consumers do not shift their electricity usage 97 percent of the consumers would still have saved money when switching to a dynamic contract. The average savings for a consumer turned out to be around 87 dollars annually. The small fraction of consumers who would have seen their costs go up it would only have gone up by around 5% compared to their annual bill.  

The data collected showed that while, as expected, consumers with a more even load profile saved more money with a dynamic contract, more unexpectedly even consumers with a peakier load profile saved money with the dynamic contract. The paper does state that these savings could partially be attributed to the fact that the rate was influenced by events in 2014 and it could therefore be the case that normally the two contracts would be more similar in costs.  

Towards flexible energy demand – Preferences for dynamic contracts, services and emissions reductions[17]

This paper investigates the households acceptance of hypothetical flexible energy contracts, aiming to increase the demand side flexibility. The results indicate that their sensitivity to electricity restrictions is higher than the sensitivity to heating restrictions. Furthermore, households require a considerable compensation to choose real time pricing over fixed fees. Next to that, other incentives such as CO2 reduction could incentivize flexibility on the demand side.

The articles concludes with: "There is a clear need in the market for automated smart home technologies that can optimize consumers’ heating or electricity consumption in such a way that no direct action from the user side is needed. Our results suggest that households are willing to participate in smart load control services; however, at the same time, they require compensation for the associated discomfort." According to our findings, the load control of heating is likely to be the low hanging fruit because the required compensations were moderate and distinctly lower relative to the respective compensations for the load control of electricity usage. Space heating also corresponds to a considerable share (approximately 70%) of the total residential energy consumption (Official Statistics of Finland, 2016b) and, hence, has the highest potential for demand side flexibility.

Large-scale assessment of mobile notifications[18]

In this paper a large scale assessment of mobile notifications is conducted. This was done through an application that enables users to connect with the computer of the user. The outcome showed that messages from different categories were valued differently. Messages from communication apps and calendars were valued high. This might be useful for our project in the sense that our messages could certainly be high-valued, since they contain information consumers would want to know. However one of the findings was also that the nature of notifications is disruptive. This is because they distract from the tasks at hand. Furthermore it is said that there is no good regulation for app developers to prevent to many messages. The apps are free to create notifications. This could result in too many messages since the app developers are stakeholders and could have as a goal to capture the users attention as much as possible. However we do not have this as our goal, since we don't benefit from repeated use of our app. This is therefore not a problem concerning our messaging.  

Hypotheses

The hypotheses are as follows:

  1. Our product will keep electricity costs of households with a dynamic contract below the electricity costs the same households would have with a variable contract, assuming the contract is started at the time of our project.
  2. Our product will keep electricity costs of households with a dynamic contract below the electricity costs the same households would have with a fixed contract, assuming that the contracted is started at the time of our project.
  3. Our product will reduce the network congestion caused by households.
  4. Our product will reduce the green energy wasted due to differences in supply and demand on the energy network.
  5. Our product will make sure households will make use of green energy rather than fossil fuelled energy.

Approach

On this wiki we will document the different ways in which these hypotheses will be tested. The first two hypotheses will be tested by comparing the energy costs of a consumer using our product with a dynamic to the energy costs of the same user having either a variable or fixed contract. This will be calculated for different scenarios to get a concise outcome of these comparisons. Hypotheses number three through five will be tested by performing literature study and interviewing involved parties.

Deliverables

The deliverables of this project will exist of a mobile phone application which will tell users when to turn on their electric appliances in order to get the lowest possible energy cost. The app will achieve this by checking for the dynamic energy pricing, having knowledge of all the electric appliances available and calculating for each appliance when it should be turned on, keeping in mind the duration that a device needs to stay on. To properly keep track of what requirements the app needs to fulfil, what requirements we would want it to fulfil and by what the developmentof the product will be constraint by we constructed three lists in which these points will be addressed.

Requirements

  • The product needs to be connected to the internet in order to know the energy prices of the coming twenty four hours;
  • The product needs a list of electrical devices along with information about the manner in which they consume energy (i.e. a dishwasher uses electricity in a cycle of a few hours, while a freezer needs to be on the entire time);
  • The product needs an algorithm that optimizes the time of use for each device in its device list;
  • The product needs an environment which the user can interact with (e.g. an application on a mobile device, a website or a device of its own);
  • The product needs to be able to send information to the user by either notifications or a screen, or the product should be able to send signals directly to the devices which should be turned on/off;
  • The user needs to be able to input information about their preffered time of receiving notifications (should this be applicable);
  • The product needs to show the money that was saved in comparison with a variable electric contract;
  • The user should not feel disrupted by the app (e.g. the app should not send notifications that would disrupt the user);
  • The environment in which the user interacts with the product should be easy to navigate, such that users with every level of experience with technology should be able to know what they are telling the app and what the app is telling them (e.g. old people, who generally have a lower level of experience with technology should not be confused by the environment).

Preferences

  • The electric devices for which the product will optimize their electricity use should all be smart devices, such that the user does not need to turn on the devices manually;

Constraints

  • The great majority of houses are, as of now, not (fully) equipped with smart devices, which means that our product should send notifications to the user telling them the time at which they should have the electric devices turn on;
  • The users will not always be close to their homes and their devices at the optimized time for their devices to be turned on. This leads to multiple constraints: the user needs to have their devices should have prepared all devices in advance before they go away from home; The devices should all either have an option to schedule the time they turn or should be smart devices.

Users

The possible users for the dynamic pricing support system, which will most likely be in the form of an app, are vast, ranging from private homeowners to businesses. Private homeowners can use this app to lower their expenses on energy, which is especially important due to the surge in energy prices due to everything happening in geopolitics. Homeowners could thus use this system to turn on their appliances at the right times leading to huge chances on saving large sums of money. Next to private homeowners even factories or businesses could look into using the algorithm, their sometimes-intensive use of energy could then also be better placed at more beneficial times. Energy intensive procedures needed to for example fabricate a certain product could then be done at better times lowering the costs of production leading to higher profits, which is in capitalism of course one of the main drivers in business. Thus, the users for the system spread almost everyone, since almost no one lives without using electricity in this current era.

Stakeholders

Private homeowners

As described above one of the most important users would be private homeowners, since the developed system would enable them to save money. This users most important wish would encompass mostly a good working app which is easy to navigate as well as a trustable system behind the app. The system should be trustable and give correct calculations on savings as well as correct times of prime use, since if the system is wrong often there would be no need to use the app. Some errors can be accepted though since in no case would using the app cost more money than when using electricity on chosen times, only a perfect human being would be able to spread the electricity usage better than the system.

Companies

The companies would similarly to the homeowners also want a good working app, with again a focus on the accuracy of the system. Companies would also benefit from other built in functions such as overviews of electricity use as well as notifications in order to inform people of the optimal usage times. Companies could in theory thus make profit from the use of the system but would not need major alterations to the normal version for private homeowners, only the inputtable devices should be customizable since companies would use machines that use amounts of electricity which are not widely known.

Other institutions

Since the use of electricity is such a general thing in life, any group of people, company or institution having control over their electricity use could use the app to lower their electricity prices. An important question that then arises however is, in the case that many people start using the app, can the system adapt to correctly spread the use of electricity. Since the increase in usage would also lead to an increase in electricity use on times that would otherwise be seen as off peak, this would then have to be counteracted by an increase in renewable energy sources. This increase would balance out the increase in load on the off peak times, leading to a greener society.

Electricity companies / Network operators

The electricity producers and network operators are also users that have to be taken into account when it comes to the system. While they may not be direct users of the system it is in theory of their best interest to have as many consumers use the system since this would lead to an increase in dynamic electricity pricing users and make the existing users more efficient. This would then lead to a decrease in load on the net on peak times and a more even spread which is positive for the network operators. The electricity companies would also have to spend less on expensive sources of electricity such as gas to fulfil peak demands since cheaper forms of electricity like solar and wind would be better used during low peak times, it could however also lead to a decrease in profits for the electricity companies since consumers can save money by using the system, if this is outweighed by the decrease in cost of electricity sources is unknown but is very important to determine the stake that electricity companies have in this system. If it would decrease total profits the electricity companies may be inclined to increase dynamic contract prices which in turn could lead to less dynamic contract users, it should thus be optimally balanced in order to have as many consumers switch to dynamic contracts.

Government

The government is also a stakeholder in the to be designed system, since the system would in theory lead to a better spread of electricity usage as well as a more optimal usage of renewable electricity sources. This increase in use of green electricity is what the government, at least with the current political parties, wishes since due to European laws the decrease in carbon emissions has to take place. This decrease in carbon emissions is thus caused by the better implementation of dynamic contracts which lead to a more optimal use of renewable electricity sources, where more of these sources would also further increase the efficiency of the whole electricity grid. Since renewable electricity is in the long run often cheaper it could also lead to financial benefits for the government since fossil fuel subsidies would no longer be needed.

Scenarios

In order to get a better view on the typical electricity usage of possible users of our product we sketched two different scenarios: A single family household consisting of two adults and one child, and a single person houshold.

Single family household consisting of two adults and one child

They turn on the dishwasher, dryer and washing machine 1 time per day. They have no electric car and no solar panels. Between 20:00 and 1:00 the TV is on. From 21:00 to 24:00 the child runs a Playstation 5. The freezer and fridge are on at all times. There is no miscellaneous electricity use in this perfect scenario.

A fixed contract would at the moment cost around 0.345 euro per kWh, a variable contract would cost around 0.37 euro per kWh. For the dynamic contract the prices of September 28 were used, these prices are in comparison to other days quite poor for dynamic contract consumers.

Let's say that the household has appliances that are quite average when it comes to electricity use. The Samsung dishwasher is always run on the eco program leading to a use of 1.053 kWh for 195 minutes, where we will assume that this is spread evenly over the time. For the dryer it is assumed that a ‘mix’ cycle is always used and that the data from 2016 is accurate for the dryer owned by this household. Thus, the dryer will use 0.66 kWh over an assumption time span of 2 hours. For the washing machine it is assumed that 0.8 kWh is used, spread also over 2 hours. For the TV it is assumed that it uses 0.2 kWh per hour. The PS5 with TV combo uses 0.3 kWh per hour. The fridge uses 0.015 kWh per hour and the freezer also uses 0.015 kWh per hour. For the dynamic contract it is assumed that the appliances are turned on at the best possible times and that the electricity use of appliances is spread evenly over the time.

Adding the entire electricity usage up found is a total use of 5.11 kWh. Thus, the cost of this electricity usage can be easily calculated for the fixed and variable contract. With a fixed contract this electricity use would cost the household 1.76 euro and with a variable contract it would cost 1.89 euro. For the dynamic case the calculation is a bit trickier since the price is different for every hour. In order to spend the least amount of money with the dynamic contract with the electricity prices of 28 September the dishwasher should be turned on at 02:00 thus it is assumed that the appliances in this case are ‘smart’ enough to be turned on automatically. The dishwasher uses 0.0054 kWh per minute. The costs of running the dishwasher from 02:00 to 05:15 is then 0.279 euro. The cost of the washing machine turns out to be 0.208 euro, for the dryer 0.172. The TV is run during fixed times and would cost for this specific day 0.332 euro. The PS5 and TV combo would cost 0.297 euro. For the fridge and freezer, the total cost is 0.228 euro. Thus, the total cost with the dynamic contract is 1.52 euro. This would mean that on a very average day the savings are 0.24 euro in comparison to a fixed contract and 0.37 euro in comparison to a variable contract. Which, assuming this is an average amount of savings, would save this household 87.6 euro on year basis comparing to a fixed contract and 135 euro comparing to a variable contract.

Single person household

This person lives alone in a small apartment, the dishwasher, washing machine and dryer are turned on once every 3 days. This person also owns an electric car which is used for commuting from and to work, needing to only be charged a small amount at home since most of the charging is done while the person is at work. In this case the electric car is a Tesla model 3 which needs to be charged 20 percent during the night. The person also powers on a TV from 20:00 to 24:00 and cooks using induction for 30 minutes at 18:00. The electricity pricing data from Oktober 1 is used where it is assumed that today is the day the person turns on their appliances.  

The person uses a washing cycle costing 1 kWh spanned over 2 hours; the dryer is put on the ‘mix’ cycle and uses 1 kWh since this dryer is quite old and thus less energy efficient. For the dishwasher the quick cycle is used, using 0.75 kWh in 30 minutes. Charging the Tesla 20% will cost 10 kWh, which takes approximately 1 hour. The induction cooktop will use 0.75 kWh. Finally, the TV will use 0.15 kWh per hour.  

For the fixed contract a price of 0.345 euro per kWh is taken and for a variable contract 0.37 euro per kWh. The total kWh consumption is 14.1 kWh, which costs with the fixed contract 4.87 euro and with the variable contract 5.22 euro.  

Choosing the best time for the dynamic contract we assume that the appliances are ‘smart’ and can thus be turned on at all times during the day. The charging of the car however has to take place during the night. The appliances are turned on at 13:00, leading to a cost of 0.51 euro for the dishwasher, dryer and washing machine. The cooking would cost 0.25 euro and the TV would cost 0.2 euro. Finally, the charging of the car will be done at 03:00 leading to a cost of 2.9 euro. This is in total a day cost of 3.86, which saves 1.01 euro in comparison to the fixed contract and 1.36 euro in comparison to the variable contract. This can however be highly influenced by geopolitics and the weather.  

The App

The design

The design board. On this canvas the ideas and the first design prototype of the app was made.

The prototyping of the design was done in Figma, a vector graphics editor specialized in web and mobile applications. This part of making the app is important because it decides how the user interact with the features of the app. If the designing of the app is not done properly then it will be difficult for users to learn and use the app as intended. A lot of thought went into making the app as simplistic with still having all the requirements that were set. The final design exists of four main pages, Home, Devices, History and Settings, the user can switch between these pages via the bottom navigation bar.

The Home-page

On the Home-page a graph with the prices for every hour is showed in a central place with the minimal, average and maximal prices. This is done so that the user can in a glance see what the energy prices for that day are. There is also at the bottom of the Home-page some place where the information of the next planned activities are shown such that the user is able to see what is going to happen and if necessary to quickly see if something needs to be changed. To finish the Home-page of a nice vector image of an house is placed at the top to make the app feel more connected to the user and to give a nice first impression of the app when opened.

The Devices-page

The Device-page shows all the added devices in a list. Each device starts off with an icon of the device and next to it the model name. Every device has an selection panel with the available cycles. The cheapest cycle is displayed in a green text colour, when a cycle is not available the text colour will be grey otherwise it will be white. Below the cycles the start and endtime is shown for the timeframes that the user has selected. Below the times a short text is shown with the cheapest starttime, or in case that that is available a text will be shown why the algorithm can not calculate the time. Next to this text is a button so that the user can indicate that he will use/set the selected cycle at the calculated time. This button also saves this information with the cost and can be seen in the History-page.

These are screenshots of the app. The first screenshot is of the Home-page, second from the Device-page, third from the History-page and the last from the Settings-page.

The History-page

The History-page has at the top an overview of the total consumed energy, the average price per kWh, the total cost of the electricity and the money saved in comparison to a variable contract. below that is an overview with the same information but than categorized per device. This is done so the user can see what devices use the most energy or are on average more expensive per kWh, these insights can be useful for the user to decrease the energy consumption and minimize the money that he spends on electricity.

The Settings-page

The Settings-page exists of multiple tabs to improve the navigation through the settings. On the first tab some general settings can be adjusted and a link to the device-settings. In the device-settings it is possible to add a device by selecting what the device is and by typing in the model of the device. When a device is added the cycles that the user want can be selected and the preferred timeframe can be set. There is also an option to set the starttime of the timeframe to now such that the user does not need to change the time if they are not sure at what time the device is always ready to start. In the settings it is also possible to change the name of a device in case of a mistake with naming the device or in case when an old device is changed with a new one. It is also possible to delete a device from the app when the wrong device was added or when the user no longer uses the device. The tabs can be navigated by clicking on the element in a list and by the icons at the top of the tab.

The technical challenges

Dynamic design

One of the challenges of building an app were the user can add devices is that the app needs to be dynamic. Programming a dynamic interface is more challenging than just a static page, because the looks of the app wil change according to the added devices and selected settings. This meant that the app had to store the devices and settings that the user added, this is not possible to do in the codefile self. The reason for this is that the code is always the same and gives the same result every time the app is turned on, the solution for this is to store all the devices and settings in a separate database to save the information. The code then reads the database, and dependent on the information in the database, and shows the appropriate information. This database also needed to be adjusted by the user from inside the app, this meant that the user needed to be able to add new devices, edit devices and delete devices. There is also the challenge that a dynamic design is more sensitive to errors, because there are a many different configurations that the user can create by selecting and adding different devices. All these configurations needed to be tested for errors/ unwanted behaviour and changed appropriate. Because of this reason is it also not possible to know for sure that the app has no errors because only the most common configurations were tested during the limited time.

The algorithm

Another challenge was implementing the algorithm in the app in a user friendly way. The main problem was that the selected timeframe was not always possible in the sense that the algorithm was not useful when the start time was already over and that the information for the next day only came available at 13:00. This meant that a smart algorithm needed to be implemented to filter out the timeframe that would lead to an error and notify the user why it was not able to calculate the best price (error prevention). And adjust the timeframe to the next day when the timeframe was already over for this day. The algorithm also had to know that when the endtime is earlier than the starttime that the user meant that the endtime is for the next day.

When a possible timeframe was put into the algorithm it had to calculate when the best starttime of the selected cycle of the device is. This is calculated by taking the correlation of the energy prices per hour and the energy usage of that particular cycle. This gave the cost for the cycle at every moment in time, we then used a simple minimum function to find the first cheapest price and the corresponding time.

Another feature of the app is that it shows which cycle is the cheapest in the selected timeframe. This is calculated by running the algorithm on the selected cycles and finding the minimum value of the all the cycles and colouring that name green. The challenge that this gave was that on opening the device-page the algorithm had to run on average three times per added device. This meant that when a user added five devices it needed to run almost fifteen times. This was a problem because the algorithm took approximately 0.8 second per device to calculate the cheapest cycle, this would lead to a load-time of four seconds to open the device-page. To solve this problem a more efficient version of the algorithm was implemented that used a convolution algorithm and flip the energy usage function of the cycle. This is in principle the same as a correlation function, but because calculating the convolution is more optimized yielded this a speed improvement of 200 times. This improvement was enough to not notice the added delay of running the algorithm multiple times.


User interviews

Interest interview

At the start of our project we interviewed a user of a dynamic contract, which can be found in Appendix B.2. Before this interview we let the interviewee sign an informed consent about the interview, which can be found in Appendix B.1. In this interview we found that he would be interested in the product, that it could be a problem that devices should be prepared at all times to be turned on at the right time and that he uses a dynamic contract because both economic and environmental reasons. We realized that we should be more specific in our interviewing style however, after we gained some more information on our product - by literature study, brainstorming and working towards a usable product -, which is why we only conducted this interview once

App testing

To get some insight into the last two requirements of our app, regarding disruptiveness and usability, we created the following interview. Herein we first inform the user about concept of dynamic contracts and its workings. After that we give the user some tasks which they should complete, to test if they can properly use the app. Would we experience that the user can not complete a part or all the tasks, we would possibly tell them an additional explanation. And at last we ask the interviewee some questions about their opinion on the app and its notifications. Before conducting the tasks and the interview we let the interviewees sign an informed consent form, which can be found in Appendix B.1.

Because we will only be interviewing Dutch people, the explanation and additional information, will first be stated in Dutch, followed by a translation.

Short explanation beforehand

in Dutch:

Een dynamisch contract is een vorm van energiecontract waarbij (in het geval van de Nederlandse variant) één dag van tevoren de energieprijzen voor de volgende dag worden vrijgegeven. Deze prijzen verschillen per uur, waarbij de prijzen vaak hoger liggen bij rond piek uren waar mensen over het algemeen veel stroom gebruiken - denk aan de tijd rond het avondeten -, maar waar de prijzen stukken lager - of zelfs negatief zijn, zodat u zelfs geld terug kan krijgen! - rond de tijd waarop weinig stroom wordt gebruikt en veel groene energie wordt opgewekt. Met onze app maken we het makkelijk om dit dynamisch contract optimaal te gebruiken, zonder zelf veel na te hoeven denken over wanneer je je apparaten aan moet zetten.

In English:

A dynamic contract is a type of energy contract where (in case of the Dutch variant) the energy prices are published one day before they apply. These prices change from hour to hour and are most likely to be higher than average at times when people generally use more electricity - around dinner time for instance-, but could be way lower - or even negative, meaning you actually get money back! - around times when generally not much energy is being used or at times when a lot of green energy is being generated. With our app we try to make it easier for consumers to optimally use this dynamic contract, without needing to remember when they have to turn on their devices themselves.

Additional information about the tasks
From left to right: Home; Operations; History; Settings; Settings->Devices

In Dutch: (we will still be refering to the screen names in English in the descriptions between brackets for the interviewer only)

(Terwijl het Home scherm wordt laten zien)

Dit is het algemene scherm. In de grafiek kunt u zien op welk moment de energie prijs het hoogste of laagste is. Aan de tekst erboven kunt u zien tot hoe hoog of laag deze prijs gaat en wat het gemiddelde is van de energieprijs vandaag. Het plaatje van het huisje is er enkel ter decoratie.

(Terwijl het Operations scherm wordt laten zien)

Hier ziet u een overzicht van je apparaten, met elke wat knoppen er onder. Met deze knoppen kunt u checken welke modus u wilt gebruiken en door op activate te drukken geeft u aan dat u een melding wilt ontvangen over het juiste moment om dit apparaat in te stellen. In de huidige versie op de computer kunnen de "start-" en "end time" niet worden aangepast, dit is mogelijk in het Settings-> Devices scherm, waar ik zo meer over vertel.

(Terwijl het History scherm wordt laten zien)

Hier ziet u een overzicht van de apparaten waarvan het gebruik is gepland. Hier kunt u zien hoeveel stroom het apparaat heeft gebruik, het gemiddelde tarief per kilowattuur, de totale kosten die het apparaat heeft gemaakt en het verschil tussen de kosten die het apparaat heeft gemaakt en de kosten die zouden worden gemaakt als het tarief altijd zou zijn als het gemiddelde.

(Terwijl het Settings scherm wordt laten zien)

Dit zijn de instellingen. Vanaf hier kunt u gaan naar de instellingen voor apparaten.

(Terwijl het Settings->Devices scherm wordt laten zien)

Op het scherm voor instellingen voor apparaten ziet u een overzicht van alle apparaten, de apparaat namen en kunt u een nieuw apparaat toevoegen om de lijst compleet te maken. Ook kunt u hier de "start-" en "end time" instellen per apparaat door eerst op het apparaat te klikken of kunt u de naam van het apparaat aanpassen. Na het aanpassen dient u de wijzingen op te slaan door op het icoontje van de floppy disk te drukken.

In English:

(While showing the Home screen)

This is the screen that appears when you open the app.In the graph you can read the highest and lowest daily price. In the text above the exact money for these prices is shown, as well as the average rate of the day. The picture of the house is only decorative.

(While showing the operations screen)

Here you can see a list of all your appliances, with some buttons underneath each one. With these buttons you can choose the cycle you want the device to run and by clicking activate you let the app know that you would like to receive a message with the right time to turn on this appliance. In the present version of our app on the computer the start and end times can not yet be adjusted, as of now you need to go to Settings->Devices if you want to change this.

(While showing history)

Here you can see an overview of the appliances of which the usage has been planned. In this overview you can see the amount of electricity used by this appliances during the selected cycle, the average rate per kilowatt hour. the total cost the appliance would have created when the average rate was applied and the difference between that price and the cost that was actually applicable

(While showing Settings)

These are the settings. From here you can navigate to the device settings.

(While showing Settings->Devices)

Here you can see an overview of all your appliances, their names and the possibility to add a new one. When you press on one of the devices you have the option to change the start and end times of the appliance and you have the option to change the name of the device. If you want to save your changes you need to press the floppy disk icon.

Tasks
  1. Check what time the hourly energy costs are highest;
  2. Check at what time you should activate the dishwasher today;
  3. Add an electric car to the devices list;
  4. Check at what time you should charge the electric car today;
  5. Check how much energy is used by running the dishwasher one time;
  6. Check how much money you have saved by charching the electric car on time.
Questions

The paper My Phone and Me tells about mobile phone notifications and how users react to this and it concludes some points which might be useful to keep in mind when designing our own product: If we want to make sure the notifications our app sends to the user are seen quickly, acted upon quickly and not cause disruptiveness for the user, the best time to send our notification is when the user has just started running errands. The first reason for this is the complexity of the task: The task may not be that complex, but it is a relatively complex task a user might perform at home, which is where they should perform the actions suggested in our notification, and the complexity of the task will lead to a shorted time that it will take the user to see the notification. The second reason for this is that a user would experience less disruptiveness when receiving a notification at the beginning of a task. One other thing we will need to keep in mind is that the user is most likely to act on our notification when it contains useful information, which is why we should make sure to present some information which is important for the user. In order to test whether this would really be the case however, we conducted the following questions:

  1. How would you feel about a notification that would remind you to check for the best time to turn on your devices?
  2. How would you feel about a notification for each device at the time that it should be turned on?
  3. Would a notification reminding you to turn on a device always be effective for you?
  4. Would a price indication alongside the notification, showing you how much money you would save if you would follow the notifications instructions and turn on a device, increase the effectiveness of the notification for you?
  5. Would the notifications be more effective for you when they would always arrive when you are already running chores?
  6. What else could increase the effectiveness of notifications for you?
  7. Would you experience disruptiveness by the number of notification per day (which is equal to the number of devices listed plus one general notification)?
  8. What number of notifications would it take for you to experience disruptiveness?
  9. Would a price indication alongside the notification decrease the level of disruptiveness?
  10. Would the level of disruptiveness due to the notifications be decreased if the notifications would always arrive when you are already doing chores?
  11. What else could decrease the level of disruptiveness due to the notifications for you?
Results

In the last weekend before the presentation in which we were going to show our app, we conducted the interview with five interviewees, three of which also tried to complete te tasks. The answers to the questions as well as the results from the tasks are documented in Appendix B.4. From the tasks the following results were found by observing and documenting the behaviour of our interviewees while performing the tasks, their feedback to the tasks and the completion percentage:

  • Checking when the energy times are highest was found to be very easy and clear.
  • Adding a device to the device list was also found to be easy for the most part, although one participant mentioned that it was initially not clear how to save this addition and suggested to add "save" in text next to the floppy disk icon.
  • Setting the timeframe for a device went wrong most of the times We already knew that this could be an issue in the version on the computer, which will not be present in the mobile version of the app.
  • The meaning of the active button was said to be a bit confusing. This will, again, be fixed in the mobile version, where the possibility of sending notifications is possible.
  • The history tab was understood well and was directly clear to all the participants

After asking the prepared questions for interviews, and possibly adding related follow up questions, we gained the following results:

  • All interviewees thought that receiving notifications would help them remember when to turn on their devices in order to optimize the time of their electricity usage.
  • Part of the respondents thought a price indication alongside the notification would help increase the effectiveness of the notification, although most did not think it would decrease disruptiveness or at least not when the mentioned amount of money was low.
  • Most of the respondents thought it would both increase the effectiveness as well as decrease the disruptiveness of the notifications when the notifications would arrive when the respondents were already running chores.
  • The respondents came up with some ideas to increase effectiveness: Create an option where you can select which devices you have already turned on after receiving the notificiton, prompting the app to not (again) remind the user of this device; let the app send a notification when a device has completed its cycle; create an overview of the money saved per month, additionally to the money saved per cycle.
  • One interviewee had the suggestion to decrease the disruptiveness of the notifications by setting a custom timeframe when they could possibly arrive, as to - for instance - not wake up the users during the morning in weekend when they could want to sleep in instead.

Discussion

The results, which are described above, answer only the questions raised after the creation of the application, leading to an analysis of the improvements that could be done to the application. These improvements are thoroughly stated above but can be summarised by the findings that notifications are a very useful addition to the application as well as that the application should be easy to understand since it could otherwise cause users to be unable to even start the use of the system. The other part of the results leads from the extensive literature research that was done, this research showed two main insights: Firstly it showed that the use of dynamic contracts lead to both cost savings as well as emission savings and secondly it showed that consumers benefit from having a type of reminder, which would in the case of the product be an application, or having their appliance use fully automated. The first point thus shows that the dynamic contracts have, in the current day, an important role to play in the reduction of emissions due to the load balancing it enacts on the power grid. The second insight shows that the creation of an application is a viable option in order to get more consumers to switch to dynamic contracts, since often the threshold that consumers find hard to cross is caused by them not wanting to alter their lifestyles too much or simply not wanting to put so much effort into the determination of the times to turn on their appliances. These results matter since it gives a foundation on which the entire idea of the application was build as well as giving insights into what should be put into the application. Without these results the application could not be further developed in the future and would lack any real sense of improvement, thus leading to a recommendation of increasing the applications understandability as well as a correct implementation of the notifications, where most importantly the notifications should be included in order to give consumers the biggest advantage when starting to use it.

While our user test and literature research thus gave much valuable information towards the effectivity of the app as well as the future improvements that should be done, it does not cover all possible future scenarios. The development of the application was completely based on the availability of a dynamic electricity contract in a world where it is still most normal to have a fixed electricity contract. In this world then consumers could switch to a dynamic contract and save on their costs as well as improving the grids load distribution by using the electricity more on the times that it is made available by renewable sources. If however in the future fossil fuel based electricity is completely fased out a dynamic contract would not add any more advantages for the electricity provider and would thus simply be unnecessary, since in this case it is only renewable energy that is available anyway. A case where the shift to a dynamic contract is made by many people could also influence the dynamic contracts and lead to the application not being valuable anymore from a cost saving point of view for the consumer. Finally the size of the effect this application will have on increasing dynamic electricy usage as well as optimisation is impossible to say from the results found in the tests and literature, thus while it shows that it is likely that use of the application reduces grid load and emissions this is not possible to quantise. Thus in future research on not the further development of the application but on the effects of dynamic contracts this could be taken into account leading hopefully in the future to a better insight to the achievements that can be made with this new way of electricity usage.

Conclusion

The goal of the project was to build a system that would lead to consumers being more willing to switch to a dynamic contract as well as consumers with a dynamic contract savings more on their electricity expenditure. This was in order to have an increase in dynamic contract users as well as a more optimized use of the contract which would then lead to better load balancing of the power grid. This load balancing is needed due to the increase in variable green electricity sources, which are on the rise due to the increasing demand for carbon emission reduction. The system build to increase this was an application that lead to the consumer having to put little to no thought into the determination of the optimal times of turning on their electrical appliances, which in turn should lead to more savings due to a better use of the dynamic contract. While the user tests did not yield any data into the capability of the app to actually save consumers money, the tests did lead to insights into the development of the app. The app was well designed but did need instructions in order to correctly use it. Furthermore notifications were said to be wished by the consumer thus leading to recommendations that should be included in a future iteration of the application. From literature research and the set up scenarios it is clear that this application would save almost every household money, since the dynamic contracts are overall less expensive than fixed contracts. For the ability of the application to reduce the load on the electricity grid conclusions have to be made from literature leading to a clear view that this application leading to an increase in use of dynamic contracts would lead to grid load reduction, enabling better and more future use of green energy.

Thus the created application would in a further developed state, where the marketing of the application is done well leading to many users, lead to a decrease in electricity costs for consumers but most importantly lead to a world where implementation of green electricity is much easier. Current ideas of a future where only green energy is available rely solely on large storages of electricity in order to cover peak load needs, but with the increased use of dynamic contracts, in part due to the availability of an application that makes the optimal use of the dynamic contracts easy, the need for these huge storages are much less since the peak load is displaced instead of displacing the electricity. A future where dynamic contracts are used mainly, with the use of the application, is thus a green world where the world is aiming at.

Appendix

Appendix A: Planning and logbook

Appendix A.1: Planning

Milestones and responsibilities
Week 1 Week 2 Week 3 Week 4 Week 5 Week 6 Week 7 Week 8 Week 9
Decide on subject of the course All
Preparing meeting agendas and

make minutes

J
State-of-the-art section L
RPC section M
Deliverables section S
Logbook and planning J
Study on cost difference between

energy contracts

S
Study on the impact of our product on

network congestion

J
Study on consumer behaviour L/M
Ideation of the design of the product S
Construct the problem statment L
Interviewing potential users M
Contact companies involved in network

congestion

J
Finalize the algorhythm needed for the product S
Summaries of literature studies on the wiki L
Make the final decicion on the programming

language used for the product

S
Analyse the interviews of users J
Analyse the contact of companies J
Finalize the app for product testing S
Prepare presentation J
Discussion J
Future research L
Appendix J
Bibliography L
Process feedback on presentation and

finalize presentation

J
Test the product M
Final presentation L
Process the product test M
Finilazation of the wiki J


Appendix A.2: Logbook

Week Name Break-down of hours Total hours spent
1 Sven Bendermacher Searing for ideas (2h), Meeting about subject (1h), Writing deliverables section and mail teachers (0.5h), finding/scanning some promising literature [1-4] (2.5h). 6
Marijn Bikker Introductory lecture, research into problems and possible technical solution, Meeting about subject, writing problem statement and RPC's. 6
Jules van Gisteren Searching for ideas (1.5h), Preparing meeting (0.5h), Meeting about subject (1h), Creating the logbook and planning (2h) 5
Lin Wolter Searching for ideas (2.5h), Looking into possible users (2h), Start of literature study with writing of State-of-the-art (3.5h) 8
2 Sven Bendermacher Meeting on Monday (2.5h), Looking at possible devices and how to use (2h), Working at the layout and design of the app (4h) 8.5
Marijn Bikker Meeting with tutors, working together on problem, literature study, meeting, literature research, user study 7.5
Jules van Gisteren Meetings (2.5h), Literature study (3h), Research on possible parties involved in subject (1.5h) 7
Lin Wolter Working in group (2h), Finding research on consumer wishes and summarising (4h), Writing problem statement and some other alterations of wiki (1.5h) 7.5
3 Sven Bendermacher Meetings (3h), Finding energy data on house appliances (2h), Finding information about smart devices connections protocols (2h), Programming a working convolution cost algorithm (3h), starting on learning to code a android app in python (2h). 12
Marijn Bikker Discussing the project, discussing the website, finishing interviews, writing informed consent form, meeting, interviewing. (more hours planned next week) 5
Jules van Gisteren Meeting on monday (2h), Meeting on thursday+preparations (1.5h), Creating planning (1h), Creating mail to Enexis and processing feedback on the mail (0.5h) 5
Lin Wolter Meetings on monday and thursday (3h) Expansion of problem statement (1h) More literature research (1.5h) Finding of data on electricity usage of appliances (2h) 7.5
4 Sven Bendermacher Meetings on today and working in group (3h), Meeting Thursday (1h), learning app-loading (1h), automating data fetching for the algorithm (2h). (Due to illness I didn't do anything during the weekend) 7
Marijn Bikker Meeting on monday and working in group (3h), Writing transcript interview(0.5h), Meeting thursday(1h), learning app-coding(2,5h) 7
Jules van Gisteren Meeting on monday and working in group (3h), Meeting thrusday (1h) 4
Lin Wolter Meeting on monday and working in group (2h), Doing interview (0.5h), Meeting thursday(1h), Writing scenarios and finding needed information(3.5h) 7
5 Sven Bendermacher Programming base layer of app (3h), Meeting on Thursday (1h), Programming setting layout (1h), Programming home page (3h). 8
Marijn Bikker Meeting on monday, programming, designing. 5.5
Jules van Gisteren Meeting on monday, Literature study, Meeting on thrusday, Rewriting requirements, Ordering the document, Rewriting and making additions to Users 8
Lin Wolter Meeting on thursday, Literature study and rewriting/ordering of state of the art. (Less meeting due to illness) 7
6 Sven Bendermacher Meeting on monday, making the app, Meeting on Thursday, finishing the app. to much
Marijn Bikker Meeting on monday, programming, Meeting on thursday, programming, working on the presentation 8
Jules van Gisteren Meeting on monday, processing feedback, partitioning tasks, meeting on thursday and partitioning tasks, fixing the citations on the wiki, reworking the section headings and order 7
Lin Wolter Meetings (3.5h), Programming cycle function (2h), Trying to turn python app into android app (4h) 9.5
7 Sven Bendermacher Meeting on monday, working in group, working on presentation, working out the notifications, preparing the demo for the presentation. 9
Marijn Bikker Meeting on monday(0.5h), working in group(1.5h), literature study(1h), meeting on thursday(1h), working on presentation(1h), user research(1h), working on presenation(1.5h). Meeting on sunday(2h), Working on presentation(0.5h) 10
Jules van Gisteren Meeting on monday, working in group, create the first version of the user tasks and interview, meeting on thursday, creating the structure of the presentation, further work out the presentation, writing text for pitch and designing the pitch Powerpoint slides, Conducting user interview, Working out user interview, preparing for the presentation 10
Lin Wolter Meeting on monday, working in group, meeting on thursday, meeting on sunday, doing user test, working on presentation, literature research. 9
8 Sven Bendermacher
Marijn Bikker Presenting(0.5h) , Meeting about partitioning of tasks(0.5) , User interviews and writing results(1h), rewriting introduction and problem statement(1h). Literature research(0.5h), Mail feedback. 3
Jules van Gisteren Presenting, Attending other presentations, Meeting about the last partitioning of tasks, User test and interview results, writing the abstract, checking for spelling mistakes, fixing the citations 8
Lin Wolter Meeting, Writing of wiki 8


Appendix B: User interviews

Appendix B.1: Informed consent interviews users

You have been asked to participate in a study for the course 0LAUK0 Project robots everywhere(2023) of Eindhoven University of Technology. This document gives you information about this study and your rights as a participant. Please read it carefully.

About the study

The aim of this study is to test the need for an algorithm and app using a dynamic electricity contract to steer the user towards low-use hours. The study will last approximately 20 minutes. In this study, you will be asked some questions about your opinion and preferences for such a tool. The experiment leader will make notes of what you are saying.

Voluntary

Your participation is completely voluntary. You can refuse to participate without giving any reasons and you can stop your participation at any time during the study. You can also withdraw your permission to use your data up to 24 hours after the study is finished. All this will have no negative consequences whatsoever.

Confidentiality

All research conducted at the Human-Technology Interaction Group adheres to the Code of Ethics of the NIP (Nederlands Instituut voor Psychologen – Dutch Institute for Psychologists). We will not be sharing personal information about you to anyone outside of the research team. No video recordings are made that could identify you. Only an audio recording will be made. The information that we collect from this study is used for writing scientific publications and will only be reported at group level. It will be completely anonymous and it cannot be traced back to you

Further information

If you want more information about this study you can ask Marijn Bikker (contact email: m.w.a.bikker@student.tue.nl). If you have any complaints about this study, please contact the supervisor, m.j.g.v.d.molengraft@tue.nl.

Certificate of Consent

I, (NAME)……………………………………….. have read and understood this consent form and have been given the opportunity to ask questions. I agree to voluntarily participate in this study carried out by group 1 of the course 0LAUK0 Project robots everywhere(2023).

Participant’s Signature: .........

Date: ...........

Appendix B.2: Explanation before interview

Een dynamisch contract is een vorm van energiecontract waarbij (in het geval van de Nederlandse variant) één dag van tevoren de energieprijzen voor de volgende dag worden vrijgegeven. Deze prijzen verschillen per uur, waarbij de prijzen vaak hoger liggen bij rond piek uren waar mensen over het algemeen veel stroom gebruiken - denk aan de tijd rond het avondeten -, maar waar de prijzen stukken lager - of zelfs negatief zijn! - rond de tijd waarop weinig stroom wordt gebruikt en veel groene energie wordt opgewekt.

https://www.dynamisch-tarief.nl/stroom/ (The site doesn't allow images to be uploaded at this time, so I'm putting this in

Appendix B.3: Interest interview

Questions
Interview mensen met een dynamisch contract:

1.      Heeft u het informed consent form begrepen gelezen?

2.     Heeft u apparaten waar je de tijd op kunt instellen voor gebruik? De vaatwasser en wasmachine bijvoorbeeld?

3.     Heeft u een veelgebruiker (elektriciteit) die ‘smart’ is? Dat wil zeggen met wifi verbinding maakt en met een app te bedienen is?

4.     Heeft u een elektrische auto?

5.     Heeft u zonnepanelen?

6.     Wat zou u vinden van een app die automatisch apparaten aanzet op de goedkoopste momenten?  

7.     Wat zou u vinden van een app die elke dag 1 of 2 meldingen stuurt over wanneer de stroom het goedkoopst is?

8.     Heeft u een dynamisch contract, zo ja wat voor soort?

9.     Wat zou u belangrijk vinden aan de app, gebruikersgemak, looks of mogelijkheid tot personalisatie?

10.   Wat zou u vinden van een feature die bijhoud hoeveel geld er dit jaar is bespaard?

Beantwoord de volgende vragen van een schaal van één tot vijf, waarbij één zeer oneens is en vijf zeer mee eens.

11.  “Ik ben overgestapt op een dynamisch contract vanwege financiële overwegingen”

12.  “Ik ben overgestapt op een dynamisch contract vanwege milieu overwegingen”

Interview mensen met een vast of variabel contract:

1.      Heeft u het informed consent form begrepen gelezen en ingevuld?

2.      Heeft u apparaten waar je de tijd op kunt instellen voor gebruik? De vaatwasser en wasmachine bijvoorbeeld?

3.      Heeft u een veelgebruiker(elektriciteit) die ‘smart’ is? Dat wil zeggen met wifi verbinding maakt en met een app te bedienen is?

4.      Heeft u een elektrische auto?

5.      Heeft u zonnepanelen?

6.      Wat zou u vinden van een app die automatisch apparaten aanzet op de goedkoopste momenten?

7.      Zou u een app interessant vinden die elke dag 1 of 2 meldingen stuurt over wanneer de stroom het goedkoopst is?

9.      Wat zou u belangrijk vinden aan de app, gebruikersgemak, looks of mogelijkheid tot personalisatie?

10.   Wat zou u vinden van een feature die bijhoud hoeveel geld er dit jaar is bespaard?

Beantwoord de volgende vragen van een schaal van één tot vijf, waarbij één zeer oneens is en vijf zeer mee eens.

11.     “Als ik zou overstappen op een dynamisch contract, dan zou ik dit doen vanwege financiële overwegingen”

12.     “Als ik zou overstappen op een dynamisch contract, dan zou ik dit doen vanwege milieu overwegingen”

Transscripts

Interview 1

interviewer: Marijn Bikker

Interviewee: Floris Bikker


Interview mensen met een dynamisch contract:


1.      Heeft u het informed consent form begrepen gelezen?

Ja die is gelezen en begrepen.


2.     Heeft u apparaten waar je de tijd op kunt instellen voor gebruik? De vaatwasser en wasmachine bijvoorbeeld?

Ja die hebben we.

3.     Heeft u een “veelgebruiker“ (elektriciteit) die ‘smart’ is? Dat wil zeggen met wifi verbinding maakt en met een app te bedienen is?

Nee dat hebben we niet.

4.     Heeft u een elektrische auto?

Nee

5.     Heeft u zonnepanelen?

Nee

6.     Wat zou u vinden van een app die automatisch apparaten aanzet op de goedkoopste momenten?

Handig, maar het zou een probleem kunnen zijn op de momenten wanneer de apparaten niet klaar zijn voor gebruik. En bovendien als je al bezig bent met de apparaten, dan is het nog maar een kleine moeite om zelf ook de tijd in te stellen wanneer hij moet draaien.

Voor elektrische auto echt handig.

Nu is het zelf in de energie-app kijken een klein beetje gedoe en gereken, een app die dat zelf doet zou misschien makkelijk zijn. Nu is het nog nieuw en leuk om in die app het even op te zoeken en uit te rekenen, misschien dat dat over een tijd minder leuk om te doen is.

7.     Wat zou u vinden van een app die elke dag 1 of 2 meldingen stuurt over wanneer de stroom het goedkoopst is?

Best handig.

8.     Heeft u een dynamisch contract, zo ja wat voor soort?

Ja, per uur variërende prijs.

9.     Waarom bent u overgestapt op een dynamisch contract, voor besparing van geld, het milieu of een andere reden?

Beiden wel. Geld en milieu. Tijdens piek uren wordt er natuurlijk elektriciteit opgewekt met fossiele brandstoffen, als wij dan elektriciteit kunnen gebruiken tijdens uren waarop er meer groene stroom is dan scheelt dat voor het milieu.

10.  Wat zou u belangrijk vinden aan de app, gebruikersgemak, looks of mogelijkheid tot personalisatie?

Gebruikersgemak het belangrijkst. Hoe de app eruit ziet niet zo.

11.  Wat zou u vinden van een feature die bijhoud hoeveel geld er dit jaar is bespaard?

Ja leuk.

Research on profit of dynamic contract

To get costumers to switch to a dynamic contract, it is important to study the possible profit of switching to such a contract.

We look at a period of at least a year, since the price one pays in the dynamic case differs a lot in the summer and winter because of the difference in electricity consumption. To compare a fixed contract with a dynamic contract, some assumptions have to be made. The most important assumption is that the market price of the electricity stays more or less the same throughout the years. The market price can change due to global (in)stability or big events, such as wars or pandemics affecting the financial market. For our comparison we assume we are dealing with a stable market price.

The business works as follows. The energy suppliers sell the electricity to the customers with a fixed price, whilst they buy the electricity on the market for fluctuating prices. In general the price the costumer pays is higher than the price the energy suppliers pay, since the energy suppliers have to make profit. But for the fixed or variable contracts the energy suppliers work with an additional safety margin for the case of rising prices. In the case of the dynamic contract there is only the small addition in price for the energy suppliers to make profit.

Then there is a risk involved in switching to a dynamic contract. You have to pay more when the market prices increase, whereas someone with a fixed contract is not affected by this, until the moment his contract expires and he has to sign a new contract.

The costumer with the dynamic contract can thus profit from the moments the market price is lower than the price the fixed contracts offer. To profit from this, the costumer must be active and use these moments to use electric devices or load their laptops and phones. The study of the European commission gives insight into the possible costumers that use a dynamic contract profitably.

Old interview that wasn't conducted: Usability requirements interview

Two requirements of our product are based on the experience of the user of the product:

  • The user should not feel disrupted by the app;
  • The environment in which the user interacts with the product should be easy to navigate, such that users with every level of experience with technology should be able to know what they are telling the app and what the app is telling them.

Because we cannot easily check if our product contains these requirements - we as creators might not feel disrupted by the product and we already have a good idea of how the app works Because we designed it - we are going to check for these requirements using an interview. Since we already want to gain some insight into this requirement before we finished a working version of the product, we are going to conduct this interview on the hypothetical use of this product. Before this interview, the interviewees will have signed the informed consent form and have been told some preliminary knowledge about the state-of-the-art of our product, which can be found in Appendix B.1 and Appendix B.2 respectively.

The following questions should be answered using the Likert scale (i.e. choosing between strongly disagree, disagree, neutral, agree or strongly agree). The questions are - as of now - written in Dutch. In the future we will include both an English and Dutch version.

  1. Een app die mij verteld wanneer het beste moment zou zijn om geld te besparen met een dynamisch contract zou mij eerder over laten stappen tot een dynamisch contract.
  2. Ik heb liever dan een algoritme checkt wat het beste moment is om mijn apparaten aan te zetten dan dat ik dat zelf zou doen.
  3. Een app die op een vast moment op de dag een notificatie stuurt op mijn telefoon wanneer ik het beste mijn apparaten (die stroom gebruiken) (vaatwasser, wasmachine, droger, etc.) aan kan zetten om geld te besparen zou ik prettig vinden.
  4. Een app die mij op een scherm in een app kan laten zien wanneer ik het beste mijn apparaten aan kan zetten zou ik prettig vinden.
  5. Ik zou liever een notificatie krijgen die mij herrinert wanneer ik mijn apparaten aan moet zetten dan dat ik zelf op een scherm moet checken.
  6. Ik zou een notificatie op een door mij zelf ingesteld op de dag kunnen instellen zodat deze altijd

Appendix B.4 User test and interview

.............

Interview Frank Bendermacher

1.     How would you feel about a notification that would remind you to check for the best time to turn on your devices?

Als ik de informatie niet te lang hoef te onthouden is dat handig.


2.     How would you feel about a notification for each device at the time that it should be turned on?

Dat is handig zodat ik daar zelf niet mee bezig hoef te zijn, dat ik nog op een bepaalt tijdstip een apparaat moet aanzetten.


3.     Would a notification reminding you to turn on a device always be effective for you?

Als de melding altijd op het juiste moment komt en alleen als het van toepassing is zou het zeker effectief zijn.


4.     Would a price indication alongside the notification, showing you how much money you would save if you would follow the notifications instructions and turn on a device, increase the effectiveness of the notification for you?

Over het algemeen denk ik dat het weinig invloed heeft of ik iets met de notificatie doe.


5.     Would the notifications be more effective for you when they would always arrive when you are already running chores?

Ja dan hoef ik de tijd niet lang te onthouden waar ik het apparaat op moet instellen.


6.     What else could increase the effectiveness of notifications for you?

Als het informatie geeft waar ik iets aan heb, op de juiste momenten.


7.     Would you experience disruptiveness by the number of notification per day (which is equal to the number of devices listed plus one general notification)?

Zoals eerder aangegeven, zolang de meldingen goede informatie geven op de juiste momenten zijn de meldingen niet storend, en ervan uitgaand dat ik 3 tot 4 apparaten in de app heb zou gemiddeld 3 meldingen per dag niet storend zijn.


8.     What number of notifications would it take for you to experience disruptiveness?

Het gaat niet zozeer over het aantal maar meer over dat ik niet twee keer dezelfde melding wil krijgen op een dag, en ik wil ook niet te veel meldingen krijgen waar ik niks aan heb.


9.     Would a price indication alongside the notification decrease the level of disruptiveness?

Nee, als de notificatie goede informatie geef over het apparaat waar ik mee bezig ben dan geeft die prijs indicatie geen meerwaarde, want als ik het apparaat ben aan het instellen dan wil ik gewoon de tijd weten wanneer de prijs het laagste is, en de desbetreffende prijs bij dat minima is dan niet een factor.


10.  Would the level of disruptiveness due to the notifications be decreased if the notifications would always arrive when you are already doing chores?

Ja zeker, zoals eerder aangegeven helpt dat met de effectiviteit van de notificatie.


11.  What else could decrease the level of disruptiveness due to the notifications for you?

Zoals het voorbeeld met de live activity notificatie dat ik nog dingen kan veranderen in de melding zonder de app te openen maakt de melding effectiever en dan dus minder storend. En ik hoef uiteraard geen melding te krijgen dat ik een apparaat moet aanzetten als ik dat op het apparaat zelf al heb ingesteld.

Interview Nicole Wolter
Tasks

1. Check what time the hourly energy costs are highest;

Easy to see, done quickly.

2. Check at what time you should activate the dishwasher today;

Bit more effort but doable. Do the home screen times even work?

3. Add an electric car to the devices list;

Easy to do, the information text helps.  

4. Check at what time you should charge the electric car today;

Doable, easy to see.  

5. Check how much energy is used by running the dishwasher one time;

Easy to see.

6. Check how much money you have saved by charging the electric car on time.

Easy to see.  


Questions

1.     How would you feel about a notification that would remind you to check for the best time to turn on your devices?

Nicole wolter: Easy. Nice to get reminded


2.     How would you feel about a notification for each device at the time that it should be turned on?

Nicole: Every device should be separate. To get the most optimised result


3.     Would a notification reminding you to turn on a device always be effective for you?

Nicole: Yes I have to be reminded of course.


4.     Would a price indication alongside the notification, showing you how much money you would save if you would follow the notifications instructions and turn on a device, increase the effectiveness of the notification for you?

Nicole: In theory it would help but if the profits are too small it may lead to less incentive.


5.     Would the notifications be more effective for you when they would always arrive when you are already running chores?

Nicole: A fixed time can also be nice but separate messages at best times is also good.


6.     What else could increase the effectiveness of notifications for you?

Nicole: I don’t know.


7.     Would you experience disruptiveness by the number of notification per day (which is equal to the number of devices listed plus one general notification)?

Nicole: I wouldn’t find this annoying.


8.     What number of notifications would it take for you to experience disruptiveness?

Nicole: Around 10 would be too much.


9.     Would a price indication alongside the notification decrease the level of disruptiveness?

Nicole: Already answered on this, does not effect.


10.  Would the level of disruptiveness due to the notifications be decreased if the notifications would always arrive when you are already doing chores?

Nicole: Would be less disruptive. Nice to set your own time for the notifications then.


11.  What else could decrease the level of disruptiveness due to the notifications for you?

Nicole: I Don’t know.


Interview Hans van Gisteren
Tasks

1. Check what time the hourly energy costs are highest.

A1: Gelukt; Meteen gezien.

2. Chek at what time you should activate the dishwasher today.

A2: Niet gelukt; Ging er van uit dat de “start time” en “end time” de momenten waren waartussen je hem het beste aan koon zetten.

3. Add an electric car to the devices list.

A3: Bijna helemaal gelukt; Niet volledig duidelijk dat het icoontje ook opslaan betekende; Stelde voor om een tekst naast het icoontje te zetten.

4. Check at what time you should charge the electric car today.

A4: Niet gelukt; Hetzelfde probleem als in taak 2. gebeurde opnieuw.

5. Check how much energy is used by running the dishwasher one time.

A5: Gelukt; meteen gezien.

6. Check how much money you have saved by charging the electric car on time.

A6: Gelukt; meteen gezien.


Na uitleg waren taak 2. en 4. ook gelukt. Wel had gaf hij bij opdracht 4. De suggestie om i.p.v. “75%. 50% 25%” ook 100% toe te voegen bij de optie voor het opladen van een elektrische auto: “Kan je hem niet helemaal opladen? Dat is wel een beetje suf toch?. Dan zou ik, bijvoorbeeld, 100%, 50% en 25% doen.”

Questions

1.     How would you feel about a notification that would remind you to check for the best time to turn on your devices?

Hans van Gisteren: Lijkt me handig.


2.     How would you feel about a notification for each device at the time that it should be turned on?

Hans: Lijkt me prima, maar 1 druk op de knop.

Jules van Gisteren: Dan moet je wel de vaatwasser aanzetten;

Hans: Dan moet je wel thuis zijn om dat te doen dus?

Jules: Dat wel, maar je kan hem van tevoren aanzetten.

Hans: Dat doe ik bv bij de wasmachine wel al, om kreukelen te voorkomen bv

Jules: Met smart devices zou dit allemaal geautomatiseerd worden

Hans: Daar zal het ook wel naar toe gaan uiteindelijk allemaal. Ook al kan ik bij de vaatwasser nog geen tijd instellen.


3.     Would a notification reminding you to turn on a device always be effective for you?

Hans: Jawel


4.     Would a price indication alongside the notification, showing you how much money you would save if you would follow the notifications instructions and turn on a device, increase the effectiveness of the notification for you?

Hans: Ja dat zou me wel extra motiveren. Ik zou het sowieso al doen, maar dat me nog wel extra motiveren; Dat is toch wel een extra drive.


5.     Would the notifications be more effective for you when they would always arrive when you are already running chores?

Hans: Dan moet ie wel weten dat je thuis bent.

Jules: Stel hij zou dat weten, zou het dan effectiever zijn?

Hans: Dan zou het opzich wel kunnen helpen


6.     What else could increase the effectiveness of notifications for you?

Hans: Dat je een functie hebt om te melding aan te vinken zodra je de taak gedaan hebt.


7.     Would you experience disruptiveness by the number of notification per day (which is equal to the number of devices listed plus one general notification)?

Hans: Er komen al zo veel appjes binnen, dus dat kan er ook wel bij. En als je het een keer mist is het ook zo’n drama.


8.     What number of notifications would it take for you to experience disruptiveness?

Hans: Boven een stuk of tien.


9.     Would a price indication alongside the notification decrease the level of disruptiveness?

Hans: Als het bijna niks scheelt zou het alsnog irritant kunnen zijn als het te veel appjes zijn, maar als het dan qua prijs flink scheelt dan mogen het ook meer appjes zijn.


10.  Would the level of disruptiveness due to the notifications be decreased if the notifications would always arrive when you are already doing chores?

Hans: Ja, ik kan anders toch niks doen op dat moment. Dan krijg ik ze het liefst ook allemaal binnen als ik thuis wat kan doen.


11.  What else could decrease the level of disruptiveness due to the notifications for you?

Hans: Dat ze niet telkens terugkomen als je de taak hebt afgerond. Dus dat je hem kan afvinken zodra je hem klaar hebt en de melding voor dat apparaat daarna stopt.

Jules: Maar de melding moet dus wel terugkomen als de taak nog niet is voltooid?

Hans: Ja dan wel?

Jules: Dan is het niet vervelend?

Hans: Nee dan niet. Misschien zou je er dan een maximum bij kunnen zetten, zodat als je de melding bijvoorbeeld al vijf keer hebt gezien dat je hem dan niet nog een keer krijgt, want dan ben je het toch niet van plan.


Interview Floris Bikker
Questions

1.    How would you feel about a notification that would remind you to check for the best time to turn on your devices?

Good idea

2.    How would you feel about a notification for each device at the time that it should be turned on?

Good idea, more effective then 1.

3.    Would a notification reminding you to turn on a device always be effective for you?

Depends on whether you are home. If I am home effective. But not in the night

4.    Would a price indication alongside the notification, showing you how much money you would save if you would follow the notifications instructions and turn on a device, increase the effectiveness of the notification for you?

Yes a little bit

5.    Would the notifications be more effective for you when they would always arrive when you are already running chores?

Effective, but I already do that right now with the ANWB app.

6.    What else could increase the effectiveness of notifications for you?

Notification if the washing machine is ready.

7.    Would you experience disruptiveness by the number of notification per day (which is equal to the number of devices listed plus one general notification)?

No 4 or 5 notifications is fine. But maybe I would like to adjust this for devices, since not every device is used everyday.

8.    What number of notifications would it take for you to experience disruptiveness?

Difficult question. That is rather something you have to experience.

9.    Would a price indication alongside the notification decrease the level of disruptiveness?

No it makes the notification more effective.

10. Would the level of disruptiveness due to the notifications be decreased if the notifications would always arrive when you are already doing chores?

No that is more effective.

11. What else could decrease the level of disruptiveness due to the notifications for you?

I cant think of anything right now.


Interview Astrid Fintelman
Tasks

The general feedback is that the app was user-friendly. Every tasks was completed successfully. However, some feedback for possible improvements is:

- Right now it looks like the timeframes can be adjusted on the 2nd screen, whether they can only be adjusted in the setting. This is confusing. The idea behind this design is however an implemented version on your phone, and on your phone you can indeed adjust the timeframe on the second screen

- It took some time to figure out how to change the starting time of the timeframe, since the button of start at current time in the settings was overlooked. After this button was found the problem was resolved

- The text activate is a little bit confusing, since it only means in the current version of the app that a cycle of the device is done. Other possible names could be: "used" or "operated".


Questions

1.    How would you feel about a notification that would remind you to check for the best time to turn on your devices?

Sounds great.

2.    How would you feel about a notification for each device at the time that it should be turned on?

Sounds great as well.

3.    Would a notification reminding you to turn on a device always be effective for you?

I would also ignore them from time to time.

4.    Would a price indication alongside the notification, showing you how much money you would save if you would follow the notifications instructions and turn on a device, increase the effectiveness of the notification for you?

No, that would be interesting after a month, but not always per cycle. Not necessary.

5.    Would the notifications be more effective for you when they would always arrive when you are already running chores?

Maybe that would result in too much notifications. For example for the dishwasher, you would open that one many times a day, and if it sends a notification every time you open it It would be disruptive.

6.    What else could increase the effectiveness of notifications for you?

What would motivate me is a notification each month or each week how much money you saved.

7.    Would you experience disruptiveness by the number of notification per day (which is equal to the number of devices listed plus one general notification)?

Yes that would be a bit much for me.

8.    What number of notifications would it take for you to experience disruptiveness?

5 already. 3 is fine.

9.    Would a price indication alongside the notification decrease the level of disruptiveness?

No since I would not be interested in those small savings.

10. Would the level of disruptiveness due to the notifications be decreased if the notifications would always arrive when you are already doing chores?

No

11. What else could decrease the level of disruptiveness due to the notifications for you?

If you can set the settings of the timing of the messages yourself. So you will not get a message to early in the morning.

Bibliography

  1. ↑ Van Hummelen, S., & Frizis, I. (2022). Research on consumer risks and benefits of dynamic electricity price contracts – A risk or an opportunity to save? Cambridge econometrics (Belgium). https://www.conpolicy.de/en/news-detail/research-on-consumer-risks-and-benefits-of-dynamic-electricity-price-contracts-a-risk-or-an-opport
  2. ↑ Boeve, S., Cherkasky, J., Bons, M., Schult, H., Nabe, C., & Kielichowska, I. (2021). ASSET study on dynamic retail electricity prices. Publications Office of the EU. https://op.europa.eu/en/publication-detail/-/publication/a8b8e55f-a17f-11eb-b85c-01aa75ed71a1/language-en
  3. ↑ Dütschke, E., & Paetz, A. (2013). Dynamic electricity pricing—Which programs do consumers prefer? Energy Policy, 59, 226–234. https://doi.org/10.1016/j.enpol.2013.03.025
  4. ↑ Schrammel, J., Diamond, L. M., Fröhlich, P., Mor, G., & Cipriano, J. (2023). Influencing residential electricity consumption with tailored messages: long-term usage patterns and effects on user experience. Energy, Sustainability and Society, 13(1). https://doi.org/10.1186/s13705-023-00386-4
  5. ↑ Tan, K. M., Ramachandaramurthy, V. K., & Yong, J. Y. (2016). Integration of electric vehicles in smart grid: A review on vehicle to grid technologies and optimization techniques. Renewable & Sustainable Energy Reviews, 53, 720–732. https://doi.org/10.1016/j.rser.2015.09.012
  6. ↑ Miletić, M., Gržanić, M., Pavić, I., Pandžić, H., & Capuder, T. (2022). The effects of household automation and dynamic electricity pricing on consumers and suppliers. Sustainable Energy, Grids and Networks, 32, 100931. https://doi.org/10.1016/j.segan.2022.100931
  7. ↑ Lago, J., Marcjasz, G., De Schutter, B., & Weron, R. (2021). Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark. Applied Energy, 293, 116983. https://doi.org/10.1016/j.apenergy.2021.116983
  8. ↑ Dupont, B. (2015, January 27). Residential demand response Based on dynamic electricity Pricing: Theory and practice. https://lirias.kuleuven.be/1731280?limo=0
  9. ↑ Finn, P., & Fitzpatrick, C. (2014). Demand side management of industrial electricity consumption: Promoting the use of renewable energy through real-time pricing. Applied Energy, 113, 11–21. https://doi.org/10.1016/j.apenergy.2013.07.003
  10. ↑ Ren, L., Yuan, M., & Jiao, X. (2023c). Electric vehicle charging and discharging scheduling strategy based on dynamic electricity price. Engineering Applications of Artificial Intelligence, 123, 106320. https://doi.org/10.1016/j.engappai.2023.106320
  11. ↑ Reichl, J. (n.d.). Report of the Quantitative Field Experiment Analysis. Horizon 2020 Research and Innovation Programme. http://www.peakapp.eu/wp-content/uploads/2019/07/D4.1.pdf
  12. ↑ McKenna, R., Abad Hernando, D., ben Brahim, T. S., Bolwig, S., & Cohen, J. (2019). Analyzing the impact of dynamic electricity prices on the Austrian energy system. https://rucforsk.ruc.dk/ws/portalfiles/portal/81003156/PEAKapp_report_Deliverable_5_2_final.pdf
  13. ↑ Mehrotra, A. K., Pejović, V., Vermeulen, J., Hendley, R. J., & Musolesi, M. (2016). My Phone and Me: Understanding People’s Receptivity to Mobile Notifications. CHI ’16: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/2858036.2858566
  14. ↑ Buryk, S., Mead, D., Mourato, S., & Torriti, J. (2015). Investigating preferences for dynamic electricity tariffs: The effect of environmental and system benefit disclosure. Energy Policy, 80, 190–195. https://doi.org/10.1016/j.enpol.2015.01.030
  15. ↑ Stelmach, G., Zanocco, C., Flora, J. A., Rajagopal, R., & Boudet, H. (2020). Exploring household energy rules and activities during peak demand to better determine potential responsiveness to time-of-use pricing. Energy Policy, 144, 111608. https://doi.org/10.1016/j.enpol.2020.111608
  16. ↑ Zethmayr, J., & Kolata, D. (2018). The costs and benefits of real-time pricing: An empirical investigation into consumer bills using hourly energy data and prices. The Electricity Journal, 31(2), 50–57. https://doi.org/10.1016/j.tej.2018.02.006
  17. ↑ Ruokamo, E., Kopsakangas-Savolainen, M., Meriläinen, T., & Svento, R. (2019). Towards flexible energy demand – Preferences for dynamic contracts, services and emissions reductions. Energy Economics, 84, 104522. https://doi.org/10.1016/j.eneco.2019.104522
  18. ↑ Shirazi, A. S., Henze, N., Dingler, T., Pielot, M., Weber, D., & Schmidt, A. (2014). Large-scale assessment of mobile notifications. CHI ’14: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/2556288.2557189


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