Unlocking predictive insights and interpretability in deep reinforcement learning for Building-Integrated Photovoltaic and Battery (BIPVB) systems

The deployment of renewable energy and the implementation of intelligent energy management strategies are crucial for decarbonizing Building Energy Systems (BES). Although data-driven Deep Reinforcement Learning (DRL) has achieved recent advancements in optimizing BES, significant challenges remain, such as the lack of studies addressing the observation space of time series data and the scarcity of interpretability. This paper first introduces future forecast information into the DRL algorithm to form the observation space for time series data. It employs Gated Recurrent Unit(GRU) and Transformer networks coupled with the DRL algorithm for operational control of a Building-Integrated Photovoltaic and Battery(BIPVB) system. Additionally, it aims to enhance the interpretability of the model regarding global and local feature importance by integrating the state-of-the-art Shapley Additive Explanations (SHAP) technique with the developed DRL model. All results were validated and tested on an open-source, real-world BIPVB system, showing that incorporating forecast information can reduce operational costs by 3.56%, while using GRU and Transformer networks to handle time-series data can further reduce costs by over 10%. The results of the SHAP value analysis demonstrated the importance of future electricity prices in forecast information for optimization, revealing the model’s complex nonlinear relationships. Additionally, this study provided interpretability for a single episode instance based on the SHAP method. Overall, the study offers an accurate, reliable, and transparent deep reinforcement learning model, along with an insightful framework for handling time-series observations in DRL.