2020 · 49 citations · 37 references
ArchitectureArchitectural EngineeringBuilding ScienceAir ConditioningHvac SystemsVentilationAir Conditioning EquipmentAir Conditioning SystemsAir Handling UnitsBuilding AutomationModel Predictive ControlReinforcement LearningAgent LearningAir Conditioning ModelingDeep Reinforcement LearningModel-based Reinforcement LearningModel-free Reinforcement LearningMulti-agent LearningMulti-agent Reinforcement LearningPredictive Control Of Buildings
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.
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Reinforcement Learning: An Introduction
Richard S. Sutton, Andy Barto · IEEE Transactions on Neural Networks · 1998 · 27.1K citations
EnergyPlus: Energy simulation program
Drury B. Crawley, Linda K. Lawrie, C.O. Pedersen et al. · 2000 · 729 citations
Engineering, Computational Engineering, Mechanical Engineering +12