Reinforcement Learning in Building Energy Management: Challenges and Future Directions

Buildings account for approximately 40% of global energy consumption and associated greenhouse gas emissions, highlighting their critical role in grid decarbonization. The integration of fluctuating renewable energy sources adds significant uncertainties, requiring adaptive energy use in buildings for grid resilience. This adaptation entails a shift from passive consumption to active participation in the energy grid, ensuring demand flexibility and occupant comfort without compromising energy efficiency.

Reinforcement Learning (RL) has emerged as a prominent approach to meet these challenges in building energy management. This presentation examines key questions pertaining to the application of RL in flexible energy management of buildings. It covers the impacts of increasing data availability, advancements in machine learning algorithms, the importance of open-source tools, and the practical aspects of software and hardware integration for effective RL implementation.

The presentation aims to provide a succinct introduction to RL in the context of building energy management, offering an overview of current research, pinpointing challenges, and highlighting opportunities for future research directions.