Multi-Agent Deep Reinforcement Learning for HVAC Control in Commercial Buildings

In commercial buildings, about 40%-50% of the total electricity consumption is attributed to Heating, Ventilation, and Air Conditioning (HVAC) systems, which places an economic burden on building operators. In this paper, we intend to minimize the energy cost of an HVAC system in a multi-zone commer...

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Bibliographic Details
Published in:	IEEE transactions on smart grid 2021-01, Vol.12 (1), p.407-419
Main Authors:	Yu, Liang, Sun, Yi, Xu, Zhanbo, Shen, Chao, Yue, Dong, Jiang, Tao, Guan, Xiaohong
Format:	Article
Language:	English
Subjects:	Air conditioning Air quality Algorithms Atmospheric modeling Buildings Commercial buildings Control algorithms Control theory Deep learning Electricity consumption Electricity pricing energy cost Energy costs Fans Heuristic algorithms HVAC HVAC equipment HVAC systems Indoor air pollution Indoor air quality indoor air quality comfort Machine learning Mathematical models multi-agent deep reinforcement learning multi-zone coordination Multiagent systems Occupancy Parameter uncertainty random occupancy Solution space Temperature Thermal comfort Thermal simulation
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