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The Economy and Policy Incorporated Computing System for Social Energy and Power Consumption Analysis

Author

Listed:
  • Hang Zhao

    (Smart Grid Research Institute, School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China)

  • Jun Zhang

    (Smart Grid Research Institute, School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China)

  • Xiaohui Wang

    (China Electric Power Research Institute, Haidian District, Beijing 100192, China)

  • Hongxia Yuan

    (Smart Grid Research Institute, School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China)

  • Tianlu Gao

    (Smart Grid Research Institute, School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China)

  • Chenxi Hu

    (Smart Grid Research Institute, School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China)

  • Jing Yan

    (Smart Grid Research Institute, School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China)

Abstract

Human activities, such as energy consumption and economic development, will significantly affect the natural environment, while changes in the natural environment will also affect the sustainability of human society. Studying the energy consumption changes of human society and forecasting medium and long-term electricity demand will help realize the sustainable development of energy in future society. However, current medium- and long-term electricity consumption forecasts have insufficient data samples and the inability to consider policy impacts. Here, we develop an Economy and Policy Incorporated Computing System (EPICS), which can use artificial intelligence technology to extract the summaries of energy policy texts automatically and calculate the importance index of energy policy. It can also process economic data of different lengths to expand samples of medium- and long-term electricity consumption forecasting effectively. A forecasting method that considers policy factors and mixed-frequency economic data is introduced to estimate future social energy and power consumption. This method has shown good forecasting ability in 27 months. The effect of EPICS can be demonstrated by predicting the medium- and long-term electricity demand.

Suggested Citation

  • Hang Zhao & Jun Zhang & Xiaohui Wang & Hongxia Yuan & Tianlu Gao & Chenxi Hu & Jing Yan, 2021. "The Economy and Policy Incorporated Computing System for Social Energy and Power Consumption Analysis," Sustainability, MDPI, vol. 13(18), pages 1-18, September.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:18:p:10473-:d:639800
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    References listed on IDEAS

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