IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v150y2018icp49-60.html
   My bibliography  Save this article

A novel data-driven approach for residential electricity consumption prediction based on ensemble learning

Author

Listed:
  • Chen, Kunlong
  • Jiang, Jiuchun
  • Zheng, Fangdan
  • Chen, Kunjin

Abstract

With the development of smart grid as well as the electricity market, it is of increasing significance to predict the household electricity consumption. In this paper, a novel data-driven framework is proposed to predict the annual household electricity consumption using ensemble learning technique. The extreme gradient boosting forest and feedforward deep networks are served as base models. These base models are combined by ridge regression. What is more, the importances of input features are estimated. A subset of features is selected as the important features to feed into the model to increase its accuracy. A comparison of the proposed ensemble framework against classical regression models indicates that the former can reduce by 30% of the prediction error. The results of this study show that ensemble learning method can be a convenient and accurate approach to predict household electricity consumption.

Suggested Citation

  • Chen, Kunlong & Jiang, Jiuchun & Zheng, Fangdan & Chen, Kunjin, 2018. "A novel data-driven approach for residential electricity consumption prediction based on ensemble learning," Energy, Elsevier, vol. 150(C), pages 49-60.
    • Handle: RePEc:eee:energy:v:150:y:2018:i:c:p:49-60
    DOI: 10.1016/j.energy.2018.02.028
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544218302561
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2018.02.028?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. Dergiades, Theologos & Tsoulfidis, Lefteris, 2008. "Estimating residential demand for electricity in the United States, 1965-2006," Energy Economics, Elsevier, vol. 30(5), pages 2722-2730, September.
    2. Alberini, Anna & Gans, Will & Velez-Lopez, Daniel, 2011. "Residential Consumption of Gas and Electricity in the U.S.: The Role of Prices and Income," Sustainable Development Papers 99637, Fondazione Eni Enrico Mattei (FEEM).
    3. Fell, Harrison & Li, Shanjun & Paul, Anthony, 2014. "A new look at residential electricity demand using household expenditure data," International Journal of Industrial Organization, Elsevier, vol. 33(C), pages 37-47.
    4. Dilaver, Zafer & Hunt, Lester C, 2011. "Modelling and forecasting Turkish residential electricity demand," Energy Policy, Elsevier, vol. 39(6), pages 3117-3127, June.
    5. Alberini, Anna & Gans, Will & Velez-Lopez, Daniel, 2011. "Residential consumption of gas and electricity in the U.S.: The role of prices and income," Energy Economics, Elsevier, vol. 33(5), pages 870-881, September.
    6. An, Ning & Zhao, Weigang & Wang, Jianzhou & Shang, Duo & Zhao, Erdong, 2013. "Using multi-output feedforward neural network with empirical mode decomposition based signal filtering for electricity demand forecasting," Energy, Elsevier, vol. 49(C), pages 279-288.
    7. 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.
    8. Zhou, Shaojie & Teng, Fei, 2013. "Estimation of urban residential electricity demand in China using household survey data," Energy Policy, Elsevier, vol. 61(C), pages 394-402.
    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. Lixin Cheng & Qiuhua Tang & Zikai Zhang & Shiqian Wu, 2021. "Data mining for fast and accurate makespan estimation in machining workshops," Journal of Intelligent Manufacturing, Springer, vol. 32(2), pages 483-500, February.
    2. Hanyao Gao & Gang Kou & Haiming Liang & Hengjie Zhang & Xiangrui Chao & Cong-Cong Li & Yucheng Dong, 2024. "Machine learning in business and finance: a literature review and research opportunities," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-35, December.
    3. Junwei Ma & Xiao Liu & Xiaoxu Niu & Yankun Wang & Tao Wen & Junrong Zhang & Zongxing Zou, 2020. "Forecasting of Landslide Displacement Using a Probability-Scheme Combination Ensemble Prediction Technique," IJERPH, MDPI, vol. 17(13), pages 1-23, July.
    4. Du, Xiaoyi & Wu, Dongdong & Yan, Yabo, 2023. "Prediction of electricity consumption based on GM(1,Nr) model in Jiangsu province, China," Energy, Elsevier, vol. 262(PA).
    5. Li, Yanying & Che, Jinxing & Yang, Youlong, 2018. "Subsampled support vector regression ensemble for short term electric load forecasting," Energy, Elsevier, vol. 164(C), pages 160-170.
    6. Zhang, Meng & Guo, Huan & Sun, Ming & Liu, Sifeng & Forrest, Jeffrey, 2022. "A novel flexible grey multivariable model and its application in forecasting energy consumption in China," Energy, Elsevier, vol. 239(PE).
    7. Kesriklioğlu, Esma & Oktay, Erkan & Karaaslan, Abdulkerim, 2023. "Predicting total household energy expenditures using ensemble learning methods," Energy, Elsevier, vol. 276(C).
    8. Hadjout, D. & Torres, J.F. & Troncoso, A. & Sebaa, A. & Martínez-Álvarez, F., 2022. "Electricity consumption forecasting based on ensemble deep learning with application to the Algerian market," Energy, Elsevier, vol. 243(C).
    9. Zhu, Xiaoyue & Dang, Yaoguo & Ding, Song, 2020. "Using a self-adaptive grey fractional weighted model to forecast Jiangsu’s electricity consumption in China," Energy, Elsevier, vol. 190(C).
    10. Sun, Chuanwang & Xu, Mengjie & Wang, Bo, 2024. "Deep learning: Spatiotemporal impact of digital economy on energy productivity," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).
    11. Paul Anton Verwiebe & Stephan Seim & Simon Burges & Lennart Schulz & Joachim Müller-Kirchenbauer, 2021. "Modeling Energy Demand—A Systematic Literature Review," Energies, MDPI, vol. 14(23), pages 1-58, November.
    12. Amir Mosavi & Mohsen Salimi & Sina Faizollahzadeh Ardabili & Timon Rabczuk & Shahaboddin Shamshirband & Annamaria R. Varkonyi-Koczy, 2019. "State of the Art of Machine Learning Models in Energy Systems, a Systematic Review," Energies, MDPI, vol. 12(7), pages 1-42, April.

    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. Jia, Jun-Jun & Guo, Jin & Wei, Chu, 2021. "Elasticities of residential electricity demand in China under increasing-block pricing constraint: New estimation using household survey data," Energy Policy, Elsevier, vol. 156(C).
    2. Mark Miller & Anna Alberini, 2015. "Sensitivity of price elasticity of demand to aggregation, unobserved heterogeneity, price trends, and price endogeneity: Evidence from U.S. Data," CER-ETH Economics working paper series 15/223, CER-ETH - Center of Economic Research (CER-ETH) at ETH Zurich.
    3. Çetinkaya, Murat & Başaran, Alparslan A. & Bağdadioğlu, Necmiddin, 2015. "Electricity reform, tariff and household elasticity in Turkey," Utilities Policy, Elsevier, vol. 37(C), pages 79-85.
    4. Miller, Mark & Alberini, Anna, 2016. "Sensitivity of price elasticity of demand to aggregation, unobserved heterogeneity, price trends, and price endogeneity: Evidence from U.S. Data," Energy Policy, Elsevier, vol. 97(C), pages 235-249.
    5. Romero-Jordán, Desiderio & del Río, Pablo & Peñasco, Cristina, 2016. "An analysis of the welfare and distributive implications of factors influencing household electricity consumption," Energy Policy, Elsevier, vol. 88(C), pages 361-370.
    6. 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.
    7. Kiran B Krishnamurthy, Chandra & Kriström, Bengt, 2013. "A cross-country analysis of residential electricity demand in 11 OECD-countries," CERE Working Papers 2013:5, CERE - the Center for Environmental and Resource Economics, revised 30 Jun 2014.
    8. Javier Bueno & Desiderio Romero-Jordán & Pablo del Río, 2020. "Analysing the Drivers of Electricity Demand in Spain after the Economic Crisis," Energies, MDPI, vol. 13(20), pages 1-18, October.
    9. Cho, Seong-Hoon & Kim, Taeyoung & Kim, Hyun Jae & Park, Kihyun & Roberts, Roland K., 2015. "Regionally-varying and regionally-uniform electricity pricing policies compared across four usage categories," Energy Economics, Elsevier, vol. 49(C), pages 182-191.
    10. Nina Boogen & Souvik Datta & Massimo Filippini, 2014. "Going beyond tradition: Estimating residential electricity demand using an appliance index and energy services," CER-ETH Economics working paper series 14/200, CER-ETH - Center of Economic Research (CER-ETH) at ETH Zurich.
    11. Xiaojia Bao, 2016. "Water, Electricity and Weather Variability in Rural Northern China," Working Papers 2014-07-02, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
    12. Boogen, Nina & Datta, Souvik & Filippini, Massimo, 2021. "Estimating residential electricity demand: New empirical evidence," Energy Policy, Elsevier, vol. 158(C).
    13. Su, Yu-Wen, 2019. "Residential electricity demand in Taiwan: Consumption behavior and rebound effect," Energy Policy, Elsevier, vol. 124(C), pages 36-45.
    14. Hung, Ming-Feng & Chie, Bin-Tzong, 2017. "The long-run performance of increasing-block pricing in Taiwan's residential electricity sector," Energy Policy, Elsevier, vol. 109(C), pages 782-793.
    15. Fell, Harrison & Linn, Joshua, 2013. "Renewable electricity policies, heterogeneity, and cost effectiveness," Journal of Environmental Economics and Management, Elsevier, vol. 66(3), pages 688-707.
    16. Wichsinee Wibulpolprasert, 2016. "Optimal Environmental Policies and Renewable Energy Investment in Electricity Markets," PIER Discussion Papers 47, Puey Ungphakorn Institute for Economic Research.
    17. Pellini, Elisabetta, 2021. "Estimating income and price elasticities of residential electricity demand with Autometrics," Energy Economics, Elsevier, vol. 101(C).
    18. Csereklyei, Zsuzsanna, 2020. "Price and income elasticities of residential and industrial electricity demand in the European Union," Energy Policy, Elsevier, vol. 137(C).
    19. Jessoe, Katrina & Rapson, David & Smith, Jeremy B., 2014. "Towards understanding the role of price in residential electricity choices: Evidence from a natural experiment," Journal of Economic Behavior & Organization, Elsevier, vol. 107(PA), pages 191-208.

    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:energy:v:150:y:2018:i:c:p:49-60. 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.journals.elsevier.com/energy .

    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.