The intelligent forecasting of the performances in PV/T collectors based on soft computing method

Solar energy has been widely used in various aspects as the greatest promising and pollution free energy comparing with other available resources in nature. Photovoltaic-thermal (PV/T) is the most generative technology, which has been invented to utilize electrical energy and heat from the solar system. The article presents a novelty of using Extreme Learning Machine (ELM) into the air type PV/T technology. For this purposes, two air type PV/T designs were fabricated and practiced for a cooling fin design in the collector and finally, collected the experimental data, which was adapted to estimate electrical and thermal efficiency for the PV/T system. Then, the results of ELM prediction model were compared with Genetic Programming (GP) and Artificial Neural Networks (ANNs) models. The experimental result was accommodated to improving the predictive accuracy of the ELM approach in comparison. Further, outcome results indicate that developed ELM models can be used satisfactorily to formulate the predictive algorithm for PV/T performances. The ELM algorithm made a good generalization, which can learn very faster comparing with other conventional popular learning algorithms. The results revealed that the improved ELM model is a well fitted tool to predict the thermal and electrical efficiency with higher accuracy.