This study used linear regression analysis, neural network and genetic neural network to build the coefficient of performance (COP) model of chiller before the condenser was cleaned respectively. The data were collected after the condenser was cleaned. The model was used to simulate the COP before the condenser was cleaned, and analyzed and compared the simulation results and improvement efficiency of the three methods under the same benchmark. The neural network used backpropagation network, whereas the genetic neural network designed appropriate fitness function according to the simulation result of backpropagation network to search for the optimum weighted value and bias value. This study used two cases for simulation comparison. The results showed that the COP of chiller of Case 1 increased by 3.82% in average, and the COP of chiller of Case 2 increased by 3.78% on average. Generally speaking, the accuracy of simulation by neural network was very high. The genetic neural network searched for the optimum weighted value and bias value according to the designed conditions, so as to achieve the optimized simulation result.
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[v1] 2015-04-14 07:18:42
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