A Unified Python Framework for Direct PPO-based Control of AHUs with Economizer Logic and CO2-Constrained Ventilation
Researchers have developed a novel Python framework utilizing Proximal Policy Optimization (PPO), a deep reinforcement learning algorithm, to optimize building HVAC systems. This framework incorporates a hierarchical logic to maintain indoor air quality by preventing CO2 levels from exceeding 1000 ppm and uses an enthalpy-based economizer for free cooling. Experimental results indicate that the PPO agent outperforms traditional PID and On-Off controllers in terms of temperature stability and energy efficiency. AI
IMPACT Enhances building energy efficiency and occupant comfort through advanced AI control strategies.