Energy consumption prediction method of energy saving building based on deep reinforcement learning

Chuan He, Ying Xiong, Yeda Lin, Lie Yu and Hui-Hua Xiong

International Journal of Global Energy Issues, 2022, vol. 44, issue 5/6, 524-536

Abstract: In order to overcome the problems of low-prediction accuracy and long prediction time of traditional building energy consumption prediction methods, this paper proposes a new energy-saving building energy consumption prediction method based on deep reinforcement learning. Through the deep reinforcement learning algorithm, a number of energy consumption behaviour return information of specific value network and strategy network are calculated, respectively to build the energy consumption probability model of energy-saving building energy consumption equipment. The linear rectification function with leakage is used to update the probability model and parameters, and the linear relationship prediction function of energy consumption parameters is constructed by using the learning process and results to complete the dynamic prediction of energy consumption of energy-saving buildings. The experimental results show that the proposed method has fast prediction speed and high accuracy, which can provide reference for the implementation of energy-saving building.

Keywords: deep intensive learning; energy saving building; energy consumption forecasting; time series; multivariate linear regression. (search for similar items in EconPapers)
Date: 2022
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