Real-time automatic control of multi-energy system for smart district community: A coupling ensemble prediction model and safe deep reinforcement learning

Energy system autonomous control is influenced by day-ahead forecasting, despite being carried out independently in the literature. This paper develops an energy management modular platform that integrates multi-variable timeseries prediction and autonomous energy infrastructure scheduling in real-time. Firstly, an ensemble prediction model is developed for the day-ahead multi-energy and renewable power prediction, which is implemented by coupling variants of CNN, GRU, and BiLSTM models into a global model using an ensemble approach. Secondly, a deep reinforcement learning (DRL) with a soft actor critic (SAC) algorithm that include safety-guided network to make the policy network constraint-aware is proposed. A multi-energy system with renewable energy and carbon capture technology is then anticipated as the energy infrastructure for achieving a carbon neutral community and evaluating our proposed model. The proposed models are trained and tested on a real-world dataset from Arizona, USA. The ensemble prediction model achieved the least root mean squared error (RMSE). On the other hand, the improved DRL method exhibits superior performance in reducing energy cost, minimum constraint violation, and fast deployment compared to state-of-the-art DRL methods. Finally, the two models are coupled and carry out generalization performance on the prediction and energy management scheme, including the sensitivity analysis on carbon capture price, in the case studies.