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

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  • 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
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    References listed on IDEAS

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    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).
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