Numerically Trained Artificial Neural Network for Experimental Performance Prediction of Air Conditioning Systems

This paper presents the development of a method for predicting the performance of air conditioning systems using few accessible and inexpensive input parameters. The cooling capacity is predicted using artificial neural network with four selected refrigerant temperatures measured from the outdoor unit as the inputs. Input output prediction data are obtained numerically and experimentally from two representative variable refrigerant flow (VRF) systems. The two systems have different characteristics and nominal capacity. The training of the ANN model is conducted with the data obtained from numerical simulations. Consequently, the ANN is tested for the prediction of the experimental cooling capacity in a quasi-certified testing equipment. The results indicate that the proposed performance prediction method demonstrates a relative error lower than 10%.