Abstract
HVAC systems account for at least 40% of the energy consumption of general office buildings. Therefore, reducing the energy use of HVAC systems is indispensable. HVAC systems used in public office buildings mostly adopt a central air conditioning system. To save energy in the central air conditioning system, high-efficiency heat source machines have been adopted, and the inverter control of pumps has been generally introduced. As a further measure to save energy, an optimal control has been proposed. Model-based control has been mainly studied. However, the target HVAC system must be appropriately equipped with sensors for model-based control to use previous operation data. To solve this problem, we proposed a model-free optimal control method using Bayesian optimization. We verified its effectiveness in one-on-one (one cooling tower and one chiller) HVAC systems. As a result, the energy-saving ratio when compared to the rated specification control is 10.38% for our proposed method and 11.34% for the model-based approach, which shows the equivalent performance. In addition, the training results indicate that the optimal set values can be automatically determined in 4 weeks as the training proceeds under rated specification control with Bayesian optimization.
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