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Communication Dans Un Congrès Année : 2023

Reinforcement Learning for Optimal HVAC Control: From Theory to Real-World Applications

Résumé

The HVAC system accounted for a significant portion of the building's energy consumption, resulting in enormous CO 2 emissions. Among the numerous HVAC control methods, reinforcement learning (RL) gives the ability to control complex systems without requiring an explicit model of the building's thermal dynamics. This study conducted a concise review of previous research on the application of RL to HVAC systems in buildings, it offered a thorough explanation of the theoretical foundations of RL and a summary of several recent RL studies that employ a particular variant of each main component of the RL: environment, state-space, action-space, rewards function, number of time steps and training episodes. Most studies construct the training environment as a stationary MDP due to the use of a predefined single sequence of transitions for non-action-controllable state vector components (e.g., outdoor temperature and occupancy schedule). This type of MDP is solved using tabular RL and DRL algorithms. Future research should focus on using the Meta-RL approach for HVAC systems, which solves the problem of non-stationarity in the environment (non-stationarity-MDP) where the sequence of transitions for (outdoor temperature and occupancy schedule) are changeable, as is the case during the real-world implementation of the RL controller in buildings.
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Dates et versions

hal-04290855 , version 1 (17-11-2023)

Identifiants

Citer

Khalil Al Sayed, Abhinandana Boodi, Roozbeh Sadeghian Broujeny, Karim Beddiar. Reinforcement Learning for Optimal HVAC Control: From Theory to Real-World Applications. IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society, Oct 2023, Singapore, Singapore. pp.1-6, ⟨10.1109/IECON51785.2023.10312131⟩. ⟨hal-04290855⟩
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