A systematic review of reinforcement learning in Building-Integrated Photovoltaic (BIPV) optimization

Abstract

Building-Integrated Photovoltaic (BIPV), as an emerging clean energy solution, plays a crucial role in energy saving, emission reduction, and grid load regulation. However, due to the uncertainty of dynamic environments and the complexity of multiple sensitive parameters, traditional scheduling methods fail to achieve optimal results. Considering that reinforcement learning, as an advanced research approach, demonstrates great potential in decision-making for high-dimensional problems and stability in dynamic environments, integrating reinforcement learning with BIPV is a feasible solution to address scheduling challenges in BIPV systems. However, there is still a lack of comprehensive analysis and systematic understanding of reinforcement learning applications in the BIPV field, which, to some extent, limits its further development in the BIPV domain. To this end, this review conducts an in-depth analysis of the effectiveness of reinforcement learning in BIPV applications from the perspective of the system construction life cycle. By considering the algorithm modeling life cycle of reinforcement learning, it comprehensively examines the potential issues in its application to BIPV, highlighting the challenges faced by existing research and future applications. Additionally, this paper integrates cutting-edge reinforcement learning knowledge, summarizes and categorizes its potential applications in BIPV, providing reference guidance for future research directions. Through this systematic review of reinforcement learning applications in the BIPV field, this study aims to offer valuable insights for subsequent research.