Parametric Studies of a Simple Direct Expansion Solar Assisted Heat Pump Using ANN and GA

Abstract

Abstract This paper reports the use of artificial neural network (ANN) integrated with genetic algorithm (GA) to predict the performance of direct expansion solar assisted heat pump (DX-SAHP), in Calicut (Latitude of 11.15 0 N, longitude of 75.49 0 E), India. The performance parameters such as power consumption, heating capacity, energy performance ratio and compressor discharge temperature of DX-SAHP obtained from the experimentation at different solar intensities and ambient temperatures are used as training data for the network. The back propagation learning algorithm with variants Lavenberg–Marguardt (LM) with 10 neurons in the hidden layer and logistic sigmoid transfer function were used in the network to predict the performance of DX-SAHP. Then those values obtained from the analysis using ANN are optimized further by integrating the ANN procedure with GA. The resulting computations confirmed that the use of ANN integrated with GA gives better optimized values compared to the value obtained from ANN for the performance prediction of DX-SAHP.