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

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

doi:10.3390/en14154525

Jie Sun, Jiao Wang + Show 5 more

Open Access

https://doi.org/10.3390/en14154525

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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.

Highlights

Electric heating is a clean, efficient, and flexible form of heating equipment
The results show that longshort term memory (LSTM) network can achieve accurate electric heating load forecasting in different time scales
On the basis of the analysis of electric heating load characteristics in distribution network, this paper focused on the analysis of meteorological factors and the comfort temperature of a main crowd

Summary

Introduction

In order to control urban haze pollution and improve the quality of life of residents, in recent years, the relevant departments of the state have launched the policies of “electricity instead of coal” and “electricity instead of oil” [1]. These policies promote the process of clean energy gradually replacing polluting energy and greatly improve the effect of reducing pollutant emissions. With the continuous improvement of residents’ requirements for indoor comfort, the scale of electric heating in winter is increasing year by year, and electric heating is used more and more frequently. The daily maximum load in winter is increasing

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