DuoBrain-HVAC: A Hybrid Autonomous Agent for Controlling HVAC Systems
The HVAC system consumes a significant portion of building energy, contributing to CO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> emissions. This paper presents DuoBrain-HVAC, a hybrid deep reinforcement learning agent, designed to optimize energy use by controlling a discrete (ON/OFF) air handling unit and a continuous (temperature setpoint) actuator. Simulated in an amphitheater using EnergyPlus, the hybrid agent combines DDQN and TD3 algorithms, achieving faster convergence and reducing energy consumption by 23.68% compared to DDQN and 16.24% compared to TD3.
Authors
- Abhinandana Boodi (ORCID: https://orcid.org/0000-0001-9179-2581)
- Roozbeh Sadeghian Broujeny (ORCID: https://orcid.org/0000-0002-6101-9819)
- Khalil Al Sayed (ORCID: https://orcid.org/0009-0007-2680-3935)
- Karim Beddiar
Institutions
- Arts et Métiers (FR)
Publication Details
- Published
- 2024-11-26
- DOI
- https://doi.org/10.1109/telfor63250.2024.10819155
- Citations
- 2
- Primary Topic
- Advanced Manufacturing and Logistics Optimization
- Type
- article
- Field-Weighted Citation Impact
- 0.70