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

Jie Sun, Jiao Wang, Yonghui Sun, Mingxin Xu, Yong Shi, Zifa Liu and Xingya Wen
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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

Energies, 2021, vol. 14, issue 15, 1-13

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.

Keywords: electric heating; load forecasting; thermal comfort; attention mechanism; LSTM neural network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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