Model-Free HVAC Optimizer based on Reinforcement Learning

Recently, there is a continues demand for embedded systems that automate buildings’ operation, such as the control of Heating Ventilation and Air-Conditioning system (HVAC) operation. These systems exhibit increased complexity and their operation relies less on human decision-making and more on computational intelligence. The efficiency of these systems is usually bounded by the orchestrators’ flexibility to optimize simultaneously multiple, and usually contrary, objectives. This paper introduces a novel framework for designing model-free orchestrators targeting to optimize the operation of HVAC systems, is introduced. The proposed orchestrator relies on Reinforcement Learning in order to support self-adaptive customization. Experimental results highlight the superiority of introduced orchestrator, as it achieves comparable performance to state-of-the-art relevant controllers without any prior detailed modeling.