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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
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    References listed on IDEAS

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    1. 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.
    2. Dilaver, Zafer & Hunt, Lester C, 2011. "Modelling and forecasting Turkish residential electricity demand," Energy Policy, Elsevier, vol. 39(6), pages 3117-3127, June.
    3. 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.
    4. 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.
    5. 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.
    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," Review of Economic Studies, Oxford University Press, vol. 72(3), pages 853-883.
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