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Smart city article details
| Title | Multi-Agent Distributed Reinforcement Learning For Energy-Efficient Thermal Comfort Control In Multi-Zone Buildings With Diverse Occupancy Patterns |
|---|---|
| ID_Doc | 38091 |
| Authors | Tariq S.; Ali U.; Kim S.; Yoo C. |
| Year | 2025 |
| Published | Energy, 332 |
| DOI | http://dx.doi.org/10.1016/j.energy.2025.137082 |
| Abstract | The rapid development of smart cities and automated infrastructures has increased building electricity demand, particularly from heating, ventilation and air conditioning (HVAC) systems. Current HVAC control methods primarily address short-term dynamics and single-zone scenarios, overlooking complexities from seasonal variability and diverse occupancy patterns in multizone buildings. Furthermore, existing data-driven frameworks lack mechanisms to transfer control policies across buildings with different thermal zone configurations. To address these limitations, this study proposes a decentralized multi-agent reinforcement learning framework for energy-efficient thermal comfort management in multizone buildings. Transfer reinforcement learning enables efficient adaptation of control strategies to buildings with differing zone configurations. Results demonstrate that occupancy and zone-specific control actions effectively balance energy efficiency and occupant comfort. The proposed method maintains thermal comfort within acceptable levels while reducing grid energy import by 51.7 % compared to conventional rule-based methods. Assigning a higher energy weight in the decentralized network structure achieved an additional 23 % reduction in energy use. The transfer learning approach successfully adapted control policies from a nine-zone office to a five-zone residential building with limited monitoring data and reduced building load by 6.4 %. Practically, this approach significantly reduces training data requirements and accelerates model deployment. Collectively, these enhancements provide building operators with effective tools to achieve significant energy savings and support city-level sustainability efforts. © 2025 Elsevier Ltd |
| Author Keywords | Decentralized control; Energy efficient HVAC control; Multizone building; Thermal comfort management; Transfer reinforcement learning |
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