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

Yuan Gao, Zehuan Hu, Shun Yamate, Junichiro Otomo, Wei-An Chen, Mingzhe Liu, Tingting Xu, Yingjun Ruan and Juan Shang

Applied Energy, 2025, vol. 384, issue C, No S0306261925001175

Abstract: 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.

Keywords: Interpretable reinforcement learning; Shapley additive explanations; Building-integrated photovoltaics and battery (BIPVB) (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261925001175
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:384:y:2025:i:c:s0306261925001175

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2025.125387

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().