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Electric Heating Load Forecasting Method Based on Improved Thermal Comfort Model and LSTM

Author

Listed:
  • Jie Sun

    (State Grid Inner Mongolia Eastern Electric Power Co., Ltd., Economic and Technical Research Institute, Hohhot 010011, China)

  • Jiao Wang

    (State Grid Inner Mongolia Eastern Electric Power Co., Ltd., Economic and Technical Research Institute, Hohhot 010011, China)

  • Yonghui Sun

    (State Grid Inner Mongolia Eastern Electric Power Co., Ltd., Economic and Technical Research Institute, Hohhot 010011, China)

  • Mingxin Xu

    (State Grid Inner Mongolia Eastern Electric Power Co., Ltd., Economic and Technical Research Institute, Hohhot 010011, China)

  • Yong Shi

    (State Grid Inner Mongolia Eastern Electric Power Co., Ltd., Economic and Technical Research Institute, Hohhot 010011, China)

  • Zifa Liu

    (School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China)

  • Xingya Wen

    (School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China)

Abstract

The accuracy of the electric heating load forecast in a new load has a close relationship with the safety and stability of distribution network in normal operation. It also has enormous implications on the architecture of a distribution network. Firstly, the thermal comfort model of the human body was established to analyze the comfortable body temperature of a main crowd under different temperatures and levels of humidity. Secondly, it analyzed the influence factors of electric heating load, and from the perspective of meteorological factors, it selected the difference between human thermal comfort temperature and actual temperature and humidity by gray correlation analysis. Finally, the attention mechanism was utilized to promote the precision of combined adjunction model, and then the data results of the predicted electric heating load were obtained. In the verification, the measured data of electric heating load in a certain area of eastern Inner Mongolia were used. The results showed that after considering the input vector with most relative factors such as temperature and human thermal comfort, the LSTM network can realize the accurate prediction of the electric heating load.

Suggested Citation

  • Jie Sun & Jiao Wang & Yonghui Sun & Mingxin Xu & Yong Shi & Zifa Liu & Xingya Wen, 2021. "Electric Heating Load Forecasting Method Based on Improved Thermal Comfort Model and LSTM," Energies, MDPI, vol. 14(15), pages 1-13, July.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:15:p:4525-:d:602182
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    References listed on IDEAS

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    1. Buratti, C. & Ricciardi, P. & Vergoni, M., 2013. "HVAC systems testing and check: A simplified model to predict thermal comfort conditions in moderate environments," Applied Energy, Elsevier, vol. 104(C), pages 117-127.
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