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Forecasting the Short-Term Electric Load Considering the Influence of Air Pollution Prevention and Control Policy via a Hybrid Model

Author

Listed:
  • Xueliang Li

    (Economic & Research Institute, State Grid Shandong Electric Power Company, Jinan 250002, China)

  • Bingkang Li

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Long Zhao

    (Economic & Research Institute, State Grid Shandong Electric Power Company, Jinan 250002, China)

  • Huiru Zhao

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Wanlei Xue

    (Economic & Research Institute, State Grid Shandong Electric Power Company, Jinan 250002, China)

  • Sen Guo

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

Abstract

Since 2013, a series of air pollution prevention and control (APPC) measures have been promulgated in China for reducing the level of air pollution, which can affect regional short-term electricity power demand by changing the behavior of power users electricity consumption. This paper analyzes the policy system of the APPC measures and its impact on regional short-term electricity demand, and determines the regional short-term load impact factors considering the impact of APPC measures. On this basis, this paper proposes a similar day selection method based on the best and worst method and grey relational analysis (BWM-GRA) in order to construct the training sample set, which considers the difference in the influence degree of characteristic indicators on daily power load. Further, a short-term load forecasting method based on least squares support vector machine (LSSVM) optimized by salp swarm algorithm (SSA) is developed. By forecasting the load of a city affected by air pollution in Northern China, and comparing the results with several selected models, it reveals that the impact of APPC measures on regional short-term load is significant. Moreover, by considering the influence of APPC measures and avoiding the subjectivity of model parameter settings, the proposed load forecasting model can improve the accuracy of, and provide an effective tool for short-term load forecasting. Finally, some limitations of this paper are discussed.

Suggested Citation

  • Xueliang Li & Bingkang Li & Long Zhao & Huiru Zhao & Wanlei Xue & Sen Guo, 2019. "Forecasting the Short-Term Electric Load Considering the Influence of Air Pollution Prevention and Control Policy via a Hybrid Model," Sustainability, MDPI, vol. 11(10), pages 1-21, May.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:10:p:2983-:d:234262
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    References listed on IDEAS

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    Cited by:

    1. Fuqiang Li & Shiying Zhang & Wenxuan Li & Wei Zhao & Bingkang Li & Huiru Zhao, 2019. "Forecasting Hourly Power Load Considering Time Division: A Hybrid Model Based on K-means Clustering and Probability Density Forecasting Techniques," Sustainability, MDPI, vol. 11(24), pages 1-17, December.

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