IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v235y2019icp900-913.html
   My bibliography  Save this article

Drivers of domestic electricity users’ price responsiveness: A novel machine learning approach

Author

Listed:
  • Guo, Peiyang
  • Lam, Jacqueline C.K.
  • Li, Victor O.K.

Abstract

Time-based pricing for domestic electricity users has been effective in reducing peak demand and facilitating integration of renewable energies. However, high cost, price non-responsiveness and adverse selection present challenges. To tackle these challenges, it would be important to investigate which users exhibit a higher potential to respond to price change, such that time-based pricing can be introduced to such users. Few studies have examined which users are more price-responsive and what drives price responsiveness. This article aims to fill this gap by comprehensively identifying the drivers that determine electricity users’ price responsiveness, in order to facilitate the selection of high potential users. We adopt a novel machine-learning approach to select the high potential users, using the Irish smart meter dataset (2009–10), which forms part of the national Time of Use trial. Our methodological novelties cover two aspects: First, using a feed-forward neural network model, we aim to determine precisely the price responsiveness of individual households, and address the nonlinearity of energy consumption and price response attributes. Second, we apply an integrated machine learning methodology to identify the drivers of price responsiveness. Our integrated approach outperforms the traditional variable selection methods by identifying drivers that are reliable. Our empirical results have shown that demographic and residential characteristics, psychological factors, historical electricity consumptionand appliance ownership are significant drivers. In particular, historical electricity consumption, income, household size, perceived behavioural control, and adoption of specific appliances, including immersion water heater and dishwasher, are significant drivers of price responsiveness across the Irish electricity users. Our results have also verified that continual price increase within a moderate range will not drive additional peak demand reduction; there is an intention-behaviour gap, where stated intention does not lead to actual peak reduction behaviours. Based on such results, we have conducted a scenario analysis to demonstrate the feasibility of selecting high potential users of achieving significant peak electricity reduction.

Suggested Citation

  • Guo, Peiyang & Lam, Jacqueline C.K. & Li, Victor O.K., 2019. "Drivers of domestic electricity users’ price responsiveness: A novel machine learning approach," Applied Energy, Elsevier, vol. 235(C), pages 900-913.
    • Handle: RePEc:eee:appene:v:235:y:2019:i:c:p:900-913
    DOI: 10.1016/j.apenergy.2018.11.014
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261918317215
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2018.11.014?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Leahy, Eimear & Lyons, Sean, 2010. "Energy use and appliance ownership in Ireland," Energy Policy, Elsevier, vol. 38(8), pages 4265-4279, August.
    2. Wang, Zhaohua & Zhang, Bin & Yin, Jianhua & Zhang, Yixiang, 2011. "Determinants and policy implications for household electricity-saving behaviour: Evidence from Beijing, China," Energy Policy, Elsevier, vol. 39(6), pages 3550-3557, June.
    3. Kursa, Miron B. & Rudnicki, Witold R., 2010. "Feature Selection with the Boruta Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 36(i11).
    4. Tso, Geoffrey K.F. & Yau, Kelvin K.W., 2007. "Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks," Energy, Elsevier, vol. 32(9), pages 1761-1768.
    5. McLoughlin, Fintan & Duffy, Aidan & Conlon, Michael, 2015. "A clustering approach to domestic electricity load profile characterisation using smart metering data," Applied Energy, Elsevier, vol. 141(C), pages 190-199.
    6. Caves, Douglas W. & Christensen, Laurits R. & Herriges, Joseph A., 1984. "Consistency of residential customer response in time-of-use electricity pricing experiments," Journal of Econometrics, Elsevier, vol. 26(1-2), pages 179-203.
    7. Gyamfi, Samuel & Krumdieck, Susan & Urmee, Tania, 2013. "Residential peak electricity demand response—Highlights of some behavioural issues," Renewable and Sustainable Energy Reviews, Elsevier, vol. 25(C), pages 71-77.
    8. Valeria Di Cosmo & Sean Lyons & Anne Nolan, 2014. "Estimating the Impact of Time-of-Use Pricing on Irish Electricity Demand," The Energy Journal, , vol. 35(2), pages 117-136, April.
    9. Kavousian, Amir & Rajagopal, Ram & Fischer, Martin, 2013. "Determinants of residential electricity consumption: Using smart meter data to examine the effect of climate, building characteristics, appliance stock, and occupants' behavior," Energy, Elsevier, vol. 55(C), pages 184-194.
    10. Newsham, Guy R. & Bowker, Brent G., 2010. "The effect of utility time-varying pricing and load control strategies on residential summer peak electricity use: A review," Energy Policy, Elsevier, vol. 38(7), pages 3289-3296, July.
    11. Krzysztof Gajowniczek & Tomasz Ząbkowski, 2015. "Data Mining Techniques for Detecting Household Characteristics Based on Smart Meter Data," Energies, MDPI, vol. 8(7), pages 1-21, July.
    12. Nicolai Meinshausen & Peter Bühlmann, 2010. "Stability selection," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(4), pages 417-473, September.
    13. Thorsnes, Paul & Williams, John & Lawson, Rob, 2012. "Consumer responses to time varying prices for electricity," Energy Policy, Elsevier, vol. 49(C), pages 552-561.
    14. Rajen D. Shah & Richard J. Samworth, 2013. "Variable selection with error control: another look at stability selection," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(1), pages 55-80, January.
    15. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
    16. O׳Connell, Niamh & Pinson, Pierre & Madsen, Henrik & O׳Malley, Mark, 2014. "Benefits and challenges of electrical demand response: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 686-699.
    17. Beckel, Christian & Sadamori, Leyna & Staake, Thorsten & Santini, Silvia, 2014. "Revealing household characteristics from smart meter data," Energy, Elsevier, vol. 78(C), pages 397-410.
    18. Jones, Rory V. & Fuertes, Alba & Lomas, Kevin J., 2015. "The socio-economic, dwelling and appliance related factors affecting electricity consumption in domestic buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 901-917.
    19. Larsen, Bodil Merethe & Nesbakken, Runa, 2004. "Household electricity end-use consumption: results from econometric and engineering models," Energy Economics, Elsevier, vol. 26(2), pages 179-200, March.
    20. Baker, Keith J. & Rylatt, R. Mark, 2008. "Improving the prediction of UK domestic energy-demand using annual consumption-data," Applied Energy, Elsevier, vol. 85(6), pages 475-482, June.
    21. Suganthi, L. & Samuel, Anand A., 2012. "Energy models for demand forecasting—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1223-1240.
    22. Ekonomou, L., 2010. "Greek long-term energy consumption prediction using artificial neural networks," Energy, Elsevier, vol. 35(2), pages 512-517.
    23. Botetzagias, Iosif & Malesios, Chrisovaladis & Poulou, Dimitra, 2014. "Electricity curtailment behaviors in Greek households: Different behaviors, different predictors," Energy Policy, Elsevier, vol. 69(C), pages 415-424.
    24. Abrahamse, Wokje & Steg, Linda, 2009. "How do socio-demographic and psychological factors relate to households' direct and indirect energy use and savings?," Journal of Economic Psychology, Elsevier, vol. 30(5), pages 711-720, October.
    25. Hartway, Rob & Price, Snuller & Woo, C.K, 1999. "Smart meter, customer choice and profitable time-of-use rate option," Energy, Elsevier, vol. 24(10), pages 895-903.
    26. Peter C. Reiss & Matthew W. White, 2005. "Household Electricity Demand, Revisited," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 72(3), pages 853-883.
    27. Tiwari, Piyush, 2000. "Architectural, Demographic, and Economic Causes of Electricity Consumption in Bombay," Journal of Policy Modeling, Elsevier, vol. 22(1), pages 81-98, January.
    28. Bradley, Peter & Leach, Matthew & Torriti, Jacopo, 2013. "A review of the costs and benefits of demand response for electricity in the UK," Energy Policy, Elsevier, vol. 52(C), pages 312-327.
    29. Guo, Peiyang & Li, Victor O.K. & Lam, Jacqueline C.K., 2017. "Smart demand response in China: Challenges and drivers," Energy Policy, Elsevier, vol. 107(C), pages 1-10.
    30. Ahmad Faruqui & Sanem Sergici, 2010. "Household response to dynamic pricing of electricity: a survey of 15 experiments," Journal of Regulatory Economics, Springer, vol. 38(2), pages 193-225, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Afzalan, Milad & Jazizadeh, Farrokh, 2019. "Residential loads flexibility potential for demand response using energy consumption patterns and user segments," Applied Energy, Elsevier, vol. 254(C).
    2. Kiguchi, Y. & Weeks, M. & Arakawa, R., 2021. "Predicting winners and losers under time-of-use tariffs using smart meter data," Energy, Elsevier, vol. 236(C).
    3. Hofmann, Matthias & Lindberg, Karen Byskov, 2024. "Evidence of households' demand flexibility in response to variable hourly electricity prices – Results from a comprehensive field experiment in Norway," Energy Policy, Elsevier, vol. 184(C).
    4. Jerzy Andruszkiewicz & Józef Lorenc & Agnieszka Weychan, 2019. "Demand Price Elasticity of Residential Electricity Consumers with Zonal Tariff Settlement Based on Their Load Profiles," Energies, MDPI, vol. 12(22), pages 1-22, November.
    5. Gržanić, M. & Capuder, T. & Zhang, N. & Huang, W., 2022. "Prosumers as active market participants: A systematic review of evolution of opportunities, models and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
    6. Chen, Peipei & Wu, Yi & Zhong, Honglin & Long, Yin & Meng, Jing, 2022. "Exploring household emission patterns and driving factors in Japan using machine learning methods," Applied Energy, Elsevier, vol. 307(C).
    7. Bampoulas, Adamantios & Pallonetto, Fabiano & Mangina, Eleni & Finn, Donal P., 2022. "An ensemble learning-based framework for assessing the energy flexibility of residential buildings with multicomponent energy systems," Applied Energy, Elsevier, vol. 315(C).
    8. Han, Yang & Lam, Jacqueline C.K. & Li, Victor O.K. & Newbery, David & Guo, Peiyang & Chan, Kelvin, 2024. "A deep learning approach for fairness-based time of use tariff design," Energy Policy, Elsevier, vol. 192(C).
    9. Xie, Yutao & Xiao, Jiang-Wen & Wang, Yan-Wu & Dong, Jiale, 2024. "A new customer selection framework for time-based pricing program," Energy, Elsevier, vol. 290(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Woo, C.K. & Li, R. & Shiu, A. & Horowitz, I., 2013. "Residential winter kWh responsiveness under optional time-varying pricing in British Columbia," Applied Energy, Elsevier, vol. 108(C), pages 288-297.
    2. Jones, Rory V. & Fuertes, Alba & Lomas, Kevin J., 2015. "The socio-economic, dwelling and appliance related factors affecting electricity consumption in domestic buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 901-917.
    3. Chalal, Moulay Larbi & Benachir, Medjdoub & White, Michael & Shahtahmassebi, Golnaz & Cumberbatch, Miranda & Shrahily, Raid, 2017. "The impact of the UK household life-cycle transitions on the electricity and gas usage patterns," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 505-518.
    4. Cansino, José M. & Dugo, Víctor & Gálvez-Ruiz, David & Román-Collado, Rocío, 2023. "What drove electricity consumption in the residential sector during the SARS-CoV-2 confinement? A special focus on university students in southern Spain," Energy, Elsevier, vol. 262(PB).
    5. Woo, C.K. & Liu, Y. & Zarnikau, J. & Shiu, A. & Luo, X. & Kahrl, F., 2018. "Price elasticities of retail energy demands in the United States: New evidence from a panel of monthly data for 2001–2016," Applied Energy, Elsevier, vol. 222(C), pages 460-474.
    6. Jieyi Kang & David Reiner, 2021. "Machine Learning on residential electricity consumption: Which households are more responsive to weather?," Working Papers EPRG2113, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    7. Woo, C.K. & Sreedharan, P. & Hargreaves, J. & Kahrl, F. & Wang, J. & Horowitz, I., 2014. "A review of electricity product differentiation," Applied Energy, Elsevier, vol. 114(C), pages 262-272.
    8. Satre-Meloy, Aven, 2019. "Investigating structural and occupant drivers of annual residential electricity consumption using regularization in regression models," Energy, Elsevier, vol. 174(C), pages 148-168.
    9. Han, Yang & Lam, Jacqueline C.K. & Li, Victor O.K. & Newbery, David & Guo, Peiyang & Chan, Kelvin, 2024. "A deep learning approach for fairness-based time of use tariff design," Energy Policy, Elsevier, vol. 192(C).
    10. Nsangou, Jean Calvin & Kenfack, Joseph & Nzotcha, Urbain & Ngohe Ekam, Paul Salomon & Voufo, Joseph & Tamo, Thomas T., 2022. "Explaining household electricity consumption using quantile regression, decision tree and artificial neural network," Energy, Elsevier, vol. 250(C).
    11. Yu, Miao & Zhao, Xintong & Gao, Yuning, 2019. "Factor decomposition of China’s industrial electricity consumption using structural decomposition analysis," Structural Change and Economic Dynamics, Elsevier, vol. 51(C), pages 67-76.
    12. Woo, C.K. & Shiu, A. & Liu, Y. & Luo, X. & Zarnikau, J., 2018. "Consumption effects of an electricity decarbonization policy: Hong Kong," Energy, Elsevier, vol. 144(C), pages 887-902.
    13. Roberts, Mike B. & Haghdadi, Navid & Bruce, Anna & MacGill, Iain, 2019. "Characterisation of Australian apartment electricity demand and its implications for low-carbon cities," Energy, Elsevier, vol. 180(C), pages 242-257.
    14. Guo, P. & Lam, J. & Li, V., 2018. "A novel machine learning approach for identifying the drivers of domestic electricity users’ price responsiveness," Cambridge Working Papers in Economics 1844, Faculty of Economics, University of Cambridge.
    15. Haider, Haider Tarish & See, Ong Hang & Elmenreich, Wilfried, 2016. "A review of residential demand response of smart grid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 166-178.
    16. Jacqueline Nicole Adams & Zsófia Deme Bélafi & Miklós Horváth & János Balázs Kocsis & Tamás Csoknyai, 2021. "How Smart Meter Data Analysis Can Support Understanding the Impact of Occupant Behavior on Building Energy Performance: A Comprehensive Review," Energies, MDPI, vol. 14(9), pages 1-23, April.
    17. Bradley, Peter & Coke, Alexia & Leach, Matthew, 2016. "Financial incentive approaches for reducing peak electricity demand, experience from pilot trials with a UK energy provider," Energy Policy, Elsevier, vol. 98(C), pages 108-120.
    18. Yarbaşı, İkram Yusuf & Çelik, Ali Kemal, 2023. "The determinants of household electricity demand in Turkey: An implementation of the Heckman Sample Selection model," Energy, Elsevier, vol. 283(C).
    19. Bing Wang & Qiran Cai & Zhenming Sun, 2020. "Determinants of Willingness to Participate in Urban Incentive-Based Energy Demand-Side Response: An Empirical Micro-Data Analysis," Sustainability, MDPI, vol. 12(19), pages 1-18, September.
    20. Huebner, Gesche & Shipworth, David & Hamilton, Ian & Chalabi, Zaid & Oreszczyn, Tadj, 2016. "Understanding electricity consumption: A comparative contribution of building factors, socio-demographics, appliances, behaviours and attitudes," Applied Energy, Elsevier, vol. 177(C), pages 692-702.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:235:y:2019:i:c:p:900-913. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.