PERFORMANCE PREDICTION OF INNOVATIVE SOLAR AIR COLLECTOR (ISAC) WITH PHASE CHANGE MATERIAL USING THE ANN APPROACH
Neeraj Mehla, Bhupinder Singh, Amit Kumar
Abstract
This study defines the modeling of innovative solar air collector (ISAC) system by utilizing the artificial neural network (ANN) approach. The aim of this work is to predict the performance of the ISAC with phase change material (PCM) (acetamide). Three parameters (time, solar intensity, and ambient temperature) were considered as input data, while the outlet air temperature of ISAC was considered as output. Experimentation was performed amid 9.00 and 24.00 h in June and July 2014 under Indian atmospheric conditions of Kurukshetra, Haryana. Then, experimental results were utilized to train the back propagation neural network (BPNN) to predict the outlet air temperature of ISAC. The results of ANN approach show that the BPNN is an effective tool and the predicted results (outlet air temperature of ISAC) are 99% in agreement with the experimental results. The payback period of the ISAC is 4.9 months which indicates the reliability of the PCM thus significantly diminishing the price of electrical energy.