Invited Talk at China Academy of Building Research (CABR) on “The Opportunities and Challenges of Reinforcement Learning for Smart Building Control”
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Reinforcement learning (RL) emerged as a transformative approach for optimizing smart building control systems, offering dynamic and adaptive solutions that significantly enhanced energy efficiency, occupant comfort, and operational sustainability. In this invited talk, the speaker delved into the evolving role of RL in the context of smart building technologies, emphasizing its potential to revolutionize how buildings responded to environmental conditions, occupancy patterns, and energy demands.
The presentation explored key opportunities brought about by RL, including its ability to process real-time data, learn from feedback, and adapt to non-linear and multi-objective optimization challenges. Examples of successful applications in HVAC systems, lighting control, and demand-side energy management were highlighted to illustrate the practical impact of RL-based solutions.
However, RL also presented notable challenges when applied to smart building control. These included issues of data sparsity, computational complexity, and the need for scalable algorithms capable of integrating diverse sensor inputs. The speaker discussed strategies to address these challenges, such as leveraging hybrid models, transfer learning, and simulation environments to accelerate deployment.
By examining these opportunities and challenges, the talk provided the audience with a balanced perspective on the feasibility of RL in advancing the field of smart building technologies. The presentation concluded with insights into future research directions, including collaborative opportunities between academia and industry to unlock the full potential of RL in creating intelligent and sustainable built environments.