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Algorithm for searching optimal set values of absorption chiller system using Bayesian optimization
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.
Algorithm for searching optimal set values of absorption chiller system using Bayesian optimization
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.
Algorithm for searching optimal set values of absorption chiller system using Bayesian optimization
Takabatake, Takuya (author) / Yamamoto, Makoto (author) / Hino, Hideitsu (author)
Science and Technology for the Built Environment ; 28 ; 188-199
2022-01-25
12 pages
Article (Journal)
Electronic Resource
Unknown
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