The predictive management in campus heating system based on deep reinforcement learning and probabilistic heat demands forecasting

Abstract

As a promising technology for replacing the rule-based decision-making in region heating systems (RHS), deep reinforcement learning (DRL) is a practical solution to identify the optimal control for heating equipment. However, as residential customers perform more casual energy-consumption behaviors, the intermittency and volatility of heat demands make managing heat supply and storage much harder for DRL agents. This study proposes a novel predictive management method for campus heating systems (CHS) with air-source heat pumps (AHP) and thermostatic water tanks. The novelty of the proposed method lies in the combination of the heat demands forecasting model and DRL-based adaptively controlling for heat supply equipment, which is firstly proposed to improve the heating supply reliability and reduce the storage dependence for CHS. Specifically, an enhanced rule, namely minimum length hamming encoding, and an input array constructing method is introduced to deal with discrete feature data and then improve the accuracy of deterministic heat demands forecasting based on long-short term memory (LSTM), and the Kernel density estimation (KDE) are employed to obtain the prediction intervals (PIs) from H-step ahead heat demands forecasting series. Followed by these, the twin delayed deep deterministic policy gradient, a model-free DRL control algorithm, is adopted for adaptively adjusting the output flow rate of AHP and then the storage of the hot water tank. To demonstrate the validity of the proposed method, a case study is presented where a campus heat demands forecasting achieves a maximum accuracy gain of 4.52%, and an optimal AHP operating controlling determined from PIs achieves a better cost reduction and supply reliability, which is superior over the conventional method using real-time heat demands or deterministic forecasting results as input.