Radial basis function and three-layered feed-forward network for the optimal output Power of photovoltaic modules

Artificial neural networks are proven to be efficient and effective methods for the purpose of modeling, estimating, optimizing, predicting and controlling in engi- neering applications. Especially for applications in photovoltaic (PV) systems, artificial neural network models have been used to estimate the optimal output power of the PV modules. Artificial neural network methods are characterized by fairly computational pro- cess, good pattern recognition potentials in terms of solving the non-linear characteristic and variability output problems of PV system. Nevertheless, there are still possible weak-nesses and shortcomings of this method during the computational process. Therefore, the research aims to develop and investigate the training process and validation of types of artificial neural networks in connection with the process of estimating the output pow- er of PV modules based on crystalline Silicon technology. The types of artificial neural network method are radial basis function (RBF) and three-layered feed-forward network (TFFN). Meanwhile, the investigated PV modules are the crystalline Silicon solar panel technologies.