Abstract
An adaptive neuro-fuzzy inference system (ANFIS) for predicting the performance of a reversibly used cooling tower (RUCT) under cross flow conditions as part of a heat pump system for a heating mode in winter was demonstrated. Extensive field experimental work was carried out in order to gather enough data for training and prediction. The statistical methods, such as the correlation coefficient, absolute fraction of variance and root mean square error, were given to compare the predicted and actual values for model validation. The simulation results predicted with the ANFIS can be used to simulate the performance of a reversibly used cooling tower quite accurately. Therefore, the ANFIS approach can reliably be used for forecasting the performance of RUCT.
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