IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v127y2020ics1364032120301337.html
   My bibliography  Save this article

Prediction of household electricity consumption and effectiveness of concerted intervention strategies based on occupant behaviour and personality traits

Author

Listed:
  • Shen, Meng
  • Lu, Yujie
  • Wei, Kua Harn
  • Cui, Qingbin

Abstract

Household electricity consumption influenced by various behavioural intervention strategies is difficult to predict due to the uncertainty that arises from human behaviours and their responses to intervention. Based on an energy conservation experiment conducted in Hangzhou, China, the study aims to develop an improved Support Vector Regression model that is capable of predicting household electricity consumption under multiple intervention strategies. This paper firstly proposes a variable selection approach to determine the best subset of consumption predictors using Akaike Information Criterion. The critical predictors of energy behaviours, personality traits, demographic/building features, weather indicators and the historical monthly consumption is identified in this process. Furthermore, this research also introduces the interaction effect between the energy behaviour and all other predictors to the Support Vector Regression model which applies Gaussian radial basis function optimised by genetic algorithm as the kernel function. More importantly, the proposed model is able to select the optimal intervention strategy and to predict the maximum electricity savings for each household. It enables the households to achieve an average reduction of 12.1% in monthly electricity consumption compared with the conventional behavioural intervention. Moreover, the Monte Carlo simulation is performed to explore the relationship between personality traits, the best-fit intervention strategies and the maximum electricity savings. The study also identifies five types of households with different combinations of extraversion and conscientiousness that respond differently to the optimised interventions. The findings of this study contribute to the residential demand-side energy management by enriching and diversifying personalised behavioural intervention strategies.

Suggested Citation

  • Shen, Meng & Lu, Yujie & Wei, Kua Harn & Cui, Qingbin, 2020. "Prediction of household electricity consumption and effectiveness of concerted intervention strategies based on occupant behaviour and personality traits," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
    • Handle: RePEc:eee:rensus:v:127:y:2020:i:c:s1364032120301337
    DOI: 10.1016/j.rser.2020.109839
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032120301337
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.rser.2020.109839?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. 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.
    2. Aydin, Erdal & Brounen, Dirk & Kok, Nils, 2018. "Information provision and energy consumption: Evidence from a field experiment," Energy Economics, Elsevier, vol. 71(C), pages 403-410.
    3. Burchell, Kevin & Rettie, Ruth & Roberts, Tom C., 2016. "Householder engagement with energy consumption feedback: the role of community action and communications," Energy Policy, Elsevier, vol. 88(C), pages 178-186.
    4. Wang, Jianzhou & Zhu, Wenjin & Zhang, Wenyu & Sun, Donghuai, 2009. "A trend fixed on firstly and seasonal adjustment model combined with the [epsilon]-SVR for short-term forecasting of electricity demand," Energy Policy, Elsevier, vol. 37(11), pages 4901-4909, November.
    5. Khosrowpour, Ardalan & Xie, Yimeng & Taylor, John E. & Hong, Yili, 2016. "One size does not fit all: Establishing the need for targeted eco-feedback," Applied Energy, Elsevier, vol. 184(C), pages 523-530.
    6. Oltra, Christian & Boso, Alex & Espluga, Josep & Prades, Ana, 2013. "A qualitative study of users' engagement with real-time feedback from in-house energy consumption displays," Energy Policy, Elsevier, vol. 61(C), pages 788-792.
    7. Frederiks, Elisha R. & Stenner, Karen & Hobman, Elizabeth V., 2015. "Household energy use: Applying behavioural economics to understand consumer decision-making and behaviour," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 1385-1394.
    8. Sari, Ramazan & Soytas, Ugur, 2004. "Disaggregate energy consumption, employment and income in Turkey," Energy Economics, Elsevier, vol. 26(3), pages 335-344, May.
    9. Schultz, P. Wesley & Estrada, Mica & Schmitt, Joseph & Sokoloski, Rebecca & Silva-Send, Nilmini, 2015. "Using in-home displays to provide smart meter feedback about household electricity consumption: A randomized control trial comparing kilowatts, cost, and social norms," Energy, Elsevier, vol. 90(P1), pages 351-358.
    10. Azar, Elie & Al Ansari, Hamad, 2017. "Framework to investigate energy conservation motivation and actions of building occupants: The case of a green campus in Abu Dhabi, UAE," Applied Energy, Elsevier, vol. 190(C), pages 563-573.
    11. 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.
    12. Anderson, Kyle & Song, Kwonsik & Lee, SangHyun & Krupka, Erin & Lee, Hyunsoo & Park, Moonseo, 2017. "Longitudinal analysis of normative energy use feedback on dormitory occupants," Applied Energy, Elsevier, vol. 189(C), pages 623-639.
    13. Kavaklioglu, Kadir, 2011. "Modeling and prediction of Turkey's electricity consumption using Support Vector Regression," Applied Energy, Elsevier, vol. 88(1), pages 368-375, January.
    14. Robert Tibshirani, 2011. "Regression shrinkage and selection via the lasso: a retrospective," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(3), pages 273-282, June.
    15. Kalogirou, Soteris A. & Bojic, Milorad, 2000. "Artificial neural networks for the prediction of the energy consumption of a passive solar building," Energy, Elsevier, vol. 25(5), pages 479-491.
    16. Kankal, Murat & AkpInar, Adem & Kömürcü, Murat Ihsan & Özsahin, Talat Sükrü, 2011. "Modeling and forecasting of Turkey's energy consumption using socio-economic and demographic variables," Applied Energy, Elsevier, vol. 88(5), pages 1927-1939, May.
    17. Wong, S.L. & Wan, Kevin K.W. & Lam, Tony N.T., 2010. "Artificial neural networks for energy analysis of office buildings with daylighting," Applied Energy, Elsevier, vol. 87(2), pages 551-557, February.
    18. Krarti, Moncef & Dubey, Kankana & Howarth, Nicholas, 2017. "Evaluation of building energy efficiency investment options for the Kingdom of Saudi Arabia," Energy, Elsevier, vol. 134(C), pages 595-610.
    19. Vu, D.H. & Muttaqi, K.M. & Agalgaonkar, A.P., 2015. "A variance inflation factor and backward elimination based robust regression model for forecasting monthly electricity demand using climatic variables," Applied Energy, Elsevier, vol. 140(C), pages 385-394.
    20. Psiloglou, B.E. & Giannakopoulos, C. & Majithia, S. & Petrakis, M., 2009. "Factors affecting electricity demand in Athens, Greece and London, UK: A comparative assessment," Energy, Elsevier, vol. 34(11), pages 1855-1863.
    21. Kua, H.W. & Wong, S.E., 2012. "Lessons for integrated household energy conservation policies from an intervention study in Singapore," Energy Policy, Elsevier, vol. 47(C), pages 49-56.
    22. Chen, Yongbao & Xu, Peng & Chu, Yiyi & Li, Weilin & Wu, Yuntao & Ni, Lizhou & Bao, Yi & Wang, Kun, 2017. "Short-term electrical load forecasting using the Support Vector Regression (SVR) model to calculate the demand response baseline for office buildings," Applied Energy, Elsevier, vol. 195(C), pages 659-670.
    23. Nilsson, Andreas & Bergstad, Cecilia Jakobsson & Thuvander, Liane & Andersson, David & Andersson, Kristin & Meiling, Pär, 2014. "Effects of continuous feedback on households’ electricity consumption: Potentials and barriers," Applied Energy, Elsevier, vol. 122(C), pages 17-23.
    24. Kontokosta, Constantine E. & Tull, Christopher, 2017. "A data-driven predictive model of city-scale energy use in buildings," Applied Energy, Elsevier, vol. 197(C), pages 303-317.
    25. Yang, YouLong & Che, JinXing & Li, YanYing & Zhao, YanJun & Zhu, SuLing, 2016. "An incremental electric load forecasting model based on support vector regression," Energy, Elsevier, vol. 113(C), pages 796-808.
    26. Fan, Cheng & Xiao, Fu & Wang, Shengwei, 2014. "Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques," Applied Energy, Elsevier, vol. 127(C), pages 1-10.
    27. Tang, Ling & Yu, Lean & Wang, Shuai & Li, Jianping & Wang, Shouyang, 2012. "A novel hybrid ensemble learning paradigm for nuclear energy consumption forecasting," Applied Energy, Elsevier, vol. 93(C), pages 432-443.
    28. Lynham, John & Nitta, Kohei & Saijo, Tatsuyoshi & Tarui, Nori, 2016. "Why does real-time information reduce energy consumption?," Energy Economics, Elsevier, vol. 54(C), pages 173-181.
    29. Chia, Yen Yee & Lee, Lam Hong & Shafiabady, Niusha & Isa, Dino, 2015. "A load predictive energy management system for supercapacitor-battery hybrid energy storage system in solar application using the Support Vector Machine," Applied Energy, Elsevier, vol. 137(C), pages 588-602.
    30. Sebastien Houde, Annika Todd, Anant Sudarshan, June A. Flora , and K. Carrie Armel, 2013. "Real-time Feedback and Electricity Consumption: A Field Experiment Assessing the Potential for Savings and Persistence," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1).
    31. Steg, Linda, 2008. "Promoting household energy conservation," Energy Policy, Elsevier, vol. 36(12), pages 4449-4453, December.
    32. Komatsu, Hidenori & Nishio, Ken-ichiro, 2015. "An experimental study on motivational change for electricity conservation by normative messages," Applied Energy, Elsevier, vol. 158(C), pages 35-43.
    33. Biswas, M.A. Rafe & Robinson, Melvin D. & Fumo, Nelson, 2016. "Prediction of residential building energy consumption: A neural network approach," Energy, Elsevier, vol. 117(P1), pages 84-92.
    34. Chen, Han & Huang, Ye & Shen, Huizhong & Chen, Yilin & Ru, Muye & Chen, Yuanchen & Lin, Nan & Su, Shu & Zhuo, Shaojie & Zhong, Qirui & Wang, Xilong & Liu, Junfeng & Li, Bengang & Tao, Shu, 2016. "Modeling temporal variations in global residential energy consumption and pollutant emissions," Applied Energy, Elsevier, vol. 184(C), pages 820-829.
    35. Azadeh, A. & Asadzadeh, S.M. & Saberi, M. & Nadimi, V. & Tajvidi, A. & Sheikalishahi, M., 2011. "A Neuro-fuzzy-stochastic frontier analysis approach for long-term natural gas consumption forecasting and behavior analysis: The cases of Bahrain, Saudi Arabia, Syria, and UAE," Applied Energy, Elsevier, vol. 88(11), pages 3850-3859.
    36. Paul C. Stern & Benjamin K. Sovacool & Thomas Dietz, 2016. "Towards a science of climate and energy choices," Nature Climate Change, Nature, vol. 6(6), pages 547-555, June.
    37. Yue, Ting & Long, Ruyin & Chen, Hong, 2013. "Factors influencing energy-saving behavior of urban households in Jiangsu Province," Energy Policy, Elsevier, vol. 62(C), pages 665-675.
    38. Fumo, Nelson & Rafe Biswas, M.A., 2015. "Regression analysis for prediction of residential energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 47(C), pages 332-343.
    39. Shen, Meng & Young, Robert & Cui, Qingbin, 2016. "The normative feedback approach for energy conservation behavior in the military community," Energy Policy, Elsevier, vol. 98(C), pages 19-32.
    40. Liu, Heping & Erdem, Ergin & Shi, Jing, 2011. "Comprehensive evaluation of ARMA-GARCH(-M) approaches for modeling the mean and volatility of wind speed," Applied Energy, Elsevier, vol. 88(3), pages 724-732, March.
    41. Cao, Guohua & Wu, Lijuan, 2016. "Support vector regression with fruit fly optimization algorithm for seasonal electricity consumption forecasting," Energy, Elsevier, vol. 115(P1), pages 734-745.
    42. Yun, Geun Young & Steemers, Koen, 2011. "Behavioural, physical and socio-economic factors in household cooling energy consumption," Applied Energy, Elsevier, vol. 88(6), pages 2191-2200, June.
    43. Vassileva, Iana & Dahlquist, Erik & Wallin, Fredrik & Campillo, Javier, 2013. "Energy consumption feedback devices’ impact evaluation on domestic energy use," Applied Energy, Elsevier, vol. 106(C), pages 314-320.
    44. Sagaert, Yves R. & Aghezzaf, El-Houssaine & Kourentzes, Nikolaos & Desmet, Bram, 2018. "Tactical sales forecasting using a very large set of macroeconomic indicators," European Journal of Operational Research, Elsevier, vol. 264(2), pages 558-569.
    45. Zhu, L. & Li, M.S. & Wu, Q.H. & Jiang, L., 2015. "Short-term natural gas demand prediction based on support vector regression with false neighbours filtered," Energy, Elsevier, vol. 80(C), pages 428-436.
    46. Wang, Jianjun & Li, Li & Niu, Dongxiao & Tan, Zhongfu, 2012. "An annual load forecasting model based on support vector regression with differential evolution algorithm," Applied Energy, Elsevier, vol. 94(C), pages 65-70.
    47. Hirotugu Akaike, 1987. "Factor analysis and AIC," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 317-332, September.
    48. Urban, Jan & Ščasný, Milan, 2012. "Exploring domestic energy-saving: The role of environmental concern and background variables," Energy Policy, Elsevier, vol. 47(C), pages 69-80.
    49. Chen, Victor L. & Delmas, Magali A. & Locke, Stephen L. & Singh, Amarjeet, 2017. "Information strategies for energy conservation: A field experiment in India," Energy Economics, Elsevier, vol. 68(C), pages 215-227.
    50. Che, JinXing & Wang, JianZhou, 2014. "Short-term load forecasting using a kernel-based support vector regression combination model," Applied Energy, Elsevier, vol. 132(C), pages 602-609.
    51. Shen, Meng & Cui, Qingbin & Fu, Liping, 2015. "Personality traits and energy conservation," Energy Policy, Elsevier, vol. 85(C), pages 322-334.
    52. Marvuglia, Antonino & Messineo, Antonio, 2012. "Monitoring of wind farms’ power curves using machine learning techniques," Applied Energy, Elsevier, vol. 98(C), pages 574-583.
    53. Østergård, Torben & Jensen, Rasmus Lund & Maagaard, Steffen Enersen, 2018. "A comparison of six metamodeling techniques applied to building performance simulations," Applied Energy, Elsevier, vol. 211(C), pages 89-103.
    54. Frank A. Wolak, 2011. "Do Residential Customers Respond to Hourly Prices? Evidence from a Dynamic Pricing Experiment," American Economic Review, American Economic Association, vol. 101(3), pages 83-87, May.
    55. Chou, Jui-Sheng & Ngo, Ngoc-Tri, 2016. "Time series analytics using sliding window metaheuristic optimization-based machine learning system for identifying building energy consumption patterns," Applied Energy, Elsevier, vol. 177(C), pages 751-770.
    56. Mathew, Paul A. & Dunn, Laurel N. & Sohn, Michael D. & Mercado, Andrea & Custudio, Claudine & Walter, Travis, 2015. "Big-data for building energy performance: Lessons from assembling a very large national database of building energy use," Applied Energy, Elsevier, vol. 140(C), pages 85-93.
    57. Wang, Qinpeng & Augenbroe, Godfried & Kim, Ji-Hyun & Gu, Li, 2016. "Meta-modeling of occupancy variables and analysis of their impact on energy outcomes of office buildings," Applied Energy, Elsevier, vol. 174(C), pages 166-180.
    58. Mirasgedis, S. & Sarafidis, Y. & Georgopoulou, E. & Lalas, D.P. & Moschovits, M. & Karagiannis, F. & Papakonstantinou, D., 2006. "Models for mid-term electricity demand forecasting incorporating weather influences," Energy, Elsevier, vol. 31(2), pages 208-227.
    59. Delmas, Magali A. & Fischlein, Miriam & Asensio, Omar I., 2013. "Information strategies and energy conservation behavior: A meta-analysis of experimental studies from 1975 to 2012," Energy Policy, Elsevier, vol. 61(C), pages 729-739.
    60. He, H.Z. & Kua, H.W., 2013. "Lessons for integrated household energy conservation policy from Singapore’s southwest Eco-living Program," Energy Policy, Elsevier, vol. 55(C), pages 105-116.
    61. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    62. Li, Danny H.W. & Yang, Liu & Lam, Joseph C., 2012. "Impact of climate change on energy use in the built environment in different climate zones – A review," Energy, Elsevier, vol. 42(1), pages 103-112.
    63. Anderson, Kyle & Lee, SangHyun, 2016. "An empirically grounded model for simulating normative energy use feedback interventions," Applied Energy, Elsevier, vol. 173(C), pages 272-282.
    64. Bianco, Vincenzo & Manca, Oronzio & Nardini, Sergio, 2009. "Electricity consumption forecasting in Italy using linear regression models," Energy, Elsevier, vol. 34(9), pages 1413-1421.
    65. Nilsson, Andreas & Andersson, Kristin & Bergstad, Cecilia Jakobsson, 2015. "Energy behaviors at the office: An intervention study on the use of equipment," Applied Energy, Elsevier, vol. 146(C), pages 434-441.
    66. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
    67. Yang, Zhang & Ce, Li & Lian, Li, 2017. "Electricity price forecasting by a hybrid model, combining wavelet transform, ARMA and kernel-based extreme learning machine methods," Applied Energy, Elsevier, vol. 190(C), pages 291-305.
    68. Wang, Zeyu & Srinivasan, Ravi S., 2017. "A review of artificial intelligence based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 796-808.
    69. Corradi, Nicola & Priftis, Konstantinos & Jacucci, Giulio & Gamberini, Luciano, 2013. "Oops, I forgot the light on! The cognitive mechanisms supporting the execution of energy saving behaviors," Journal of Economic Psychology, Elsevier, vol. 34(C), pages 88-96.
    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. Marcel Antal & Vlad Mihailescu & Tudor Cioara & Ionut Anghel, 2022. "Blockchain-Based Distributed Federated Learning in Smart Grid," Mathematics, MDPI, vol. 10(23), pages 1-19, November.
    2. Juana Isabel Méndez & Adán Medina & Pedro Ponce & Therese Peffer & Alan Meier & Arturo Molina, 2022. "Evolving Gamified Smart Communities in Mexico to Save Energy in Communities through Intelligent Interfaces," Energies, MDPI, vol. 15(15), pages 1-29, July.
    3. Elsisi, Mahmoud & Amer, Mohammed & Dababat, Alya’ & Su, Chun-Lien, 2023. "A comprehensive review of machine learning and IoT solutions for demand side energy management, conservation, and resilient operation," Energy, Elsevier, vol. 281(C).
    4. Nitin Kumar Singh & Takuya Fukushima & Masaaki Nagahara, 2023. "Gradient Boosting Approach to Predict Energy-Saving Awareness of Households in Kitakyushu," Energies, MDPI, vol. 16(16), pages 1-10, August.
    5. Wang, Yuanping & Hou, Lingchun & Cai, Weiguang & Zhou, Zhaoyin & Bian, Jing, 2023. "Exploring the drivers and influencing mechanisms of urban household electricity consumption in China - Based on longitudinal data at the provincial level," Energy, Elsevier, vol. 273(C).
    6. Gołębiowska, Bernadeta & Bartczak, Anna & Budziński, Wiktor, 2021. "Impact of social comparison on preferences for Demand Side Management in Poland," Energy Policy, Elsevier, vol. 149(C).
    7. Salem, Mohammed Z. & Ertz, Myriam & Sarigӧllü, Emine, 2021. "Demarketing strategies to rationalize electricity consumption in the Gaza Strip-Palestine," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    8. Wang, Zeyu & Liu, Jian & Zhang, Yuanxin & Yuan, Hongping & Zhang, Ruixue & Srinivasan, Ravi S., 2021. "Practical issues in implementing machine-learning models for building energy efficiency: Moving beyond obstacles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    9. Razak Olu-Ajayi & Hafiz Alaka & Hakeem Owolabi & Lukman Akanbi & Sikiru Ganiyu, 2023. "Data-Driven Tools for Building Energy Consumption Prediction: A Review," Energies, MDPI, vol. 16(6), pages 1-20, March.
    10. Qian-Cheng Wang & Yi-Xuan Wang & Izzy Yi Jian & Hsi-Hsien Wei & Xuan Liu & Yao-Tian Ma, 2020. "Exploring the “Energy-Saving Personality Traits” in the Office and Household Situation: An Empirical Study," Energies, MDPI, vol. 13(14), pages 1-17, July.
    11. Gao, Feng & Chi, Hong & Shao, Xueyan, 2021. "Forecasting residential electricity consumption using a hybrid machine learning model with online search data," Applied Energy, Elsevier, vol. 300(C).
    12. Uzziah Mutumbi & Gladman Thondhlana & Sheunesu Ruwanza, 2022. "Co-Designed Interventions Yield Significant Electricity Savings among Low-Income Households in Makhanda South Africa," Energies, MDPI, vol. 15(7), pages 1-17, March.
    13. 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).
    14. Mi, Lingyun & Xu, Ting & Sun, Yuhuan & Yang, Hang & Wang, Bangjun & Gan, Xiaoli & Qiao, Lijie, 2021. "Promoting differentiated energy savings: Analysis of the psychological motivation of households with different energy consumption levels," Energy, Elsevier, vol. 218(C).
    15. Yuanping Wang & Weiguang Cai & Lingchun Hou & Zhaoyin Zhou & Jing Bian, 2022. "Examining the Provincial-Level Difference and Impact Factors of Urban Household Electricity Consumption in China—Based on the Extended STIRPAT Model," Sustainability, MDPI, vol. 14(16), pages 1-18, August.
    16. Piselli, Cristina & Salvadori, Giacomo & Diciotti, Lorenzo & Fantozzi, Fabio & Pisello, Anna Laura, 2021. "Assessing users’ willingness-to-engagement towards Net Zero Energy communities in Italy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).
    17. Karol Bot & Samira Santos & Inoussa Laouali & Antonio Ruano & Maria da Graça Ruano, 2021. "Design of Ensemble Forecasting Models for Home Energy Management Systems," Energies, MDPI, vol. 14(22), pages 1-37, November.

    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. Spandagos, Constantine & Baark, Erik & Ng, Tze Ling & Yarime, Masaru, 2021. "Social influence and economic intervention policies to save energy at home: Critical questions for the new decade and evidence from air-condition use," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    2. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
    3. Spandagos, Constantine & Yarime, Masaru & Baark, Erik & Ng, Tze Ling, 2020. "“Triple Target” policy framework to influence household energy behavior: Satisfy, strengthen, include," Applied Energy, Elsevier, vol. 269(C).
    4. Shao, Zhen & Chao, Fu & Yang, Shan-Lin & Zhou, Kai-Le, 2017. "A review of the decomposition methodology for extracting and identifying the fluctuation characteristics in electricity demand forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 123-136.
    5. Shen, Meng & Li, Xiang & Lu, Yujie & Cui, Qingbin & Wei, Yi-Ming, 2021. "Personality-based normative feedback intervention for energy conservation," Energy Economics, Elsevier, vol. 104(C).
    6. Khosrowpour, Ardalan & Jain, Rishee K. & Taylor, John E. & Peschiera, Gabriel & Chen, Jiayu & Gulbinas, Rimas, 2018. "A review of occupant energy feedback research: Opportunities for methodological fusion at the intersection of experimentation, analytics, surveys and simulation," Applied Energy, Elsevier, vol. 218(C), pages 304-316.
    7. 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.
    8. Yang, Youlong & Che, Jinxing & Deng, Chengzhi & Li, Li, 2019. "Sequential grid approach based support vector regression for short-term electric load forecasting," Applied Energy, Elsevier, vol. 238(C), pages 1010-1021.
    9. Anderson, Kyle & Song, Kwonsik & Lee, SangHyun & Krupka, Erin & Lee, Hyunsoo & Park, Moonseo, 2017. "Longitudinal analysis of normative energy use feedback on dormitory occupants," Applied Energy, Elsevier, vol. 189(C), pages 623-639.
    10. Ahir, Rajesh K. & Chakraborty, Basab, 2021. "A meta-analytic approach for determining the success factors for energy conservation," Energy, Elsevier, vol. 230(C).
    11. Mi, Lingyun & Gan, Xiaoli & Sun, Yuhuan & Lv, Tao & Qiao, Lijie & Xu, Ting, 2021. "Effects of monetary and nonmonetary interventions on energy conservation: A meta-analysis of experimental studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    12. Valor, Carmen & Escudero, Carmen & Labajo, Victoria & Cossent, Rafael, 2019. "Effective design of domestic energy efficiency displays: A proposed architecture based on empirical evidence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    13. Chou, Jui-Sheng & Tran, Duc-Son, 2018. "Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders," Energy, Elsevier, vol. 165(PB), pages 709-726.
    14. Sylwia Słupik & Joanna Kos-Łabędowicz & Joanna Trzęsiok, 2021. "Energy-Related Behaviour of Consumers from the Silesia Province (Poland)—Towards a Low-Carbon Economy," Energies, MDPI, vol. 14(8), pages 1-23, April.
    15. Ma, Weiwu & Fang, Song & Liu, Gang & Zhou, Ruoyu, 2017. "Modeling of district load forecasting for distributed energy system," Applied Energy, Elsevier, vol. 204(C), pages 181-205.
    16. Ben-Haim, Yakov, 2021. "Feedback for energy conservation: An info-gap approach," Energy, Elsevier, vol. 223(C).
    17. Andor, Mark A. & Fels, Katja M., 2018. "Behavioral Economics and Energy Conservation – A Systematic Review of Non-price Interventions and Their Causal Effects," Ecological Economics, Elsevier, vol. 148(C), pages 178-210.
    18. Fathi, Soheil & Srinivasan, Ravi & Fenner, Andriel & Fathi, Sahand, 2020. "Machine learning applications in urban building energy performance forecasting: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    19. Batalla-Bejerano, Joan & Trujillo-Baute, Elisa & Villa-Arrieta, Manuel, 2020. "Smart meters and consumer behaviour: Insights from the empirical literature," Energy Policy, Elsevier, vol. 144(C).
    20. Wang, Zheng-Xin & Li, Qin & Pei, Ling-Ling, 2018. "A seasonal GM(1,1) model for forecasting the electricity consumption of the primary economic sectors," Energy, Elsevier, vol. 154(C), pages 522-534.

    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:rensus:v:127:y:2020:i:c:s1364032120301337. 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/600126/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.