Volume 18: Sustainable Energy Solutions for a Post-COVID Recovery towards a Better Future: Part I

Application of LSTM to Determine the Water Supply Temperature in Central Heating Systems Wang zeyu, Cui xuyang, Ynag Junhong

Abstract

Based on the heat demand of users, adjusting the water supply temperature and regulating the heating system can achieve matching the heat dissipation of heat dissipation equipment of heat users with the demand heat load of users and prevent energy wastage caused by high room temperature. This paper proposes a model and method for determining the water supply temperature of heat sources based on load and flow constraints for specific engineering cases, and uses LSTM deep neural network and multiple regression to simulate and analyze the water supply temperature. The results show that the deviation of LSTM is 7.22% compared to the actual value, which is much lower than the 18.20% of multiple regression.

Keywords LSTM, multiple regression, water supply temperature model, regulation, central heating system