Building Heat Demand Prediction Based on Reinforcement Learning for Thermal Comfort Management

Author:

Wang Chendong,Zheng Lihong,Yuan Jianjuan,Huang Ke,Zhou Zhihua

Abstract

The accurate prediction of building heat demand plays the critical role in refined management of heating, which is the basis for on-demand heating operation. This paper proposed a prediction model framework for building heat demand based on reinforcement learning. The environment, reward function and agent of the model were established, and experiments were carried out to verify the effectiveness and advancement of the model. Through the building heat demand prediction, the model proposed in this study can dynamically control the indoor temperature within the acceptable interval (19–23 °C). Moreover, the experimental results showed that after the model reached the primary, intermediate and advanced targets in training, the proportion of time that the indoor temperature can be controlled within the target interval (20.5–21.5 °C) was over 35%, 55% and 70%, respectively. In addition to maintaining indoor temperature, the model proposed in this study also achieved on-demand heating operation. The model achieving the advanced target, which had the best indoor temperature control performance, only had a supply–demand error of 4.56%.

Funder

Tianjin Science and Technology Commission

Publisher

MDPI AG

Subject

Energy (miscellaneous),Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment,Electrical and Electronic Engineering,Control and Optimization,Engineering (miscellaneous),Building and Construction

Reference37 articles.

1. Responding to Climate Change: China’s Policies and Actions;The State Council Information Office of the People’s Republic of China,2021

2. Evaluation of the operation data for improving the prediction accuracy of heating parameters in heating substation

3. Annual Report on China Building Energy Efficiency,2021

4. Thermal comfort in naturally ventilated buildings in hot-humid area of China

5. Characteristics of residential energy consumption in China: Findings from a household survey

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