Optimization methods using artificial intelligence algorithms to estimate thermal efficiency of PV/T system

摘要

Renewable energies, specifically solar energy has been employed in numerous applications while being CO 2 emission free energy in comparison with fossil fuel resources. The main purpose of this study is to predict thermal efficiency of photovoltaic‐thermal (PV/T) setups in regard with input temperature, recirculation flow rate, and solar irradiation by modifying multilayer perceptron artificial neural network (MLP‐ANN), adaptive neuro‐fuzzy inference system (ANFIS), and least squares support vector machine (LSSVM) approaches. For this goal, more than 100 empirical measurements were performed on a fabricated water‐cooled PV/T setup. Several numerical analyses are also carried out to assess the validity of the presented models. It is confirmed that there is a great agreement between predictive models and actual data. The proposed ANN model provided the best performance due to the mean squared error (MSE) and determination coefficient (R 2 ) values of 0.009 and 1.00, respectively. Also, numerical comparisons with other recently developed models were performed.