Abstract
The daily performance of a CO2 heat pump water heating system with a hot water storage tank is affected by the history of daily hot water demand and heat pump operating conditions. To attain the maximum system performance, it is important to estimate the daily changes in the system performance values accurately in relation to those in hot water demand and heat pump operating conditions, and determine the operating conditions optimally based on the estimation. In this paper, neural network models are used for this estimation, and the values of model parameters are identified by a global optimization method. In addition, the outlet water temperature for during operation and the inlet water temperature for shutdown are determined to maximize the system efficiency subject to a lower limit for the volume of unused hot water. The validity and effectiveness of this approach are ascertained by a numerical study using a simulated hot water demand.
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