Solar radiation forecasting using artificial neural network

Solar radiation is the basic energy source on planet earth. In order to estimate and forecast the solar radiation, it is vital to trace sun's orbit, climatic conditions and dissipation of rays. The function of solar photovoltaic systems is to convert solar energy into electric power. The output power relies on approaching radiation and few features of the intended solar panel. Currently, photovoltaic power is generated in larger amounts. It is necessary that the forecasted data could be efficiently used for controlling and running electricity gauze and to merchandise solar power. In this proposed method, ANNs are used to formulate the solar radiation prediction models. Two different datasets are gathered. The normalization, training and testing processes are done on the gathered historical data. The method used here is supervised learning. The implementation is done by Back Propagation algorithm and Radial basis function network and the results are compared. The prediction accuracy of this method has been studied by various error definitions. The result obtained has greater coincidence between the calculated and the estimated values.