A radial basis function neural network based approach for the electrical characteristics estimation of a photovoltaic module
This work addresses the need for more accurate design tools in photovoltaic engineering, but it is incremental as it applies an existing neural network method to a specific domain problem.
The paper tackled the problem of accurately predicting the electrical output behavior of photovoltaic modules by applying a radial basis function neural network (RBFNN) model, resulting in improved accuracy for I-V and P-V curves using solar irradiation and temperature data with simulation and experimental validation.
The design process of photovoltaic (PV) modules can be greatly enhanced by using advanced and accurate models in order to predict accurately their electrical output behavior. The main aim of this paper is to investigate the application of an advanced neural network based model of a module to improve the accuracy of the predicted output I--V and P--V curves and to keep in account the change of all the parameters at different operating conditions. Radial basis function neural networks (RBFNN) are here utilized to predict the output characteristic of a commercial PV module, by reading only the data of solar irradiation and temperature. A lot of available experimental data were used for the training of the RBFNN, and a backpropagation algorithm was employed. Simulation and experimental validation is reported.