CO₂ ヒートポンプ給湯システムにおける性能日変化の推定

—一定環境および運転条件下における推定—

抄録

The daily performance of a water heating system composed of a CO2 heat pump and a hot water storage tank is affected by the history of ambient conditions, hot water demand, and operating conditions as well as the resultant history of temperature distribution of the storage tank. To operate the system optimally under daily changes in these items, it is important to accurately estimate daily changes in the values of performance criteria such as COP, storage and system efficiencies, and volumes of stored and unused hot water. In this paper, the daily changes in the values of performance criteria corresponding to that in hot water demand are estimated under daily constant ambient and operating conditions by multilayered neural network models, and the values of model parameters are identified by a global optimization method to heighten the estimation accuracy. A numerical study is conducted using the values of performance criteria obtained by a numerical simulation for simulated monthly hot water demands, and the validity and effectiveness of this approach are ascertained.