MARCO - Multi-Agent Reinforcement learning based COntrol of building HVAC systems

Abstract

Optimal control of building heating, ventilation, air-conditioning (HVAC) equipment has typically been based on rules and model-based predictive control (MPC). Challenges in developing accurate models of buildings render these approaches sub-optimal and unstable in real-life operations. Model-free Deep Reinforcement Learning (DRL) approaches have been proposed very recently to address this. However, existing works on DRL for HVAC suffer from some limitations. First, they consider buildings with few HVAC units, thus leaving open the question of scale. Second, they consider only air-side control of air-handling-units (AHUs) without taking into the water-side chiller control, though chillers account for a significant portion of HVAC energy. Third, they use a single learning agent that adjusts multiple set-points of the HVAC system.