One day ahead forecasting of energy generating in photovoltaic systems

Abstract

The article presents selected methods for forecasting energy generated by a solar system. Short-term forecasts are necessary in planning the work of renewable energy sources and their share in the energy market. Forecasting from the one-day horizon is one of the short-term forecasts. Rear-round prognostic models have been designed using various forecasting methods such as regression, neural networks or time series. On the basis of one day ahead forecasts the accuracy of designed models was assessed. The influence of selected weather factors on forecasts accuracy is also presented, only for models implemented by MLP neural networks. As well as the results of research on the impact of the model structure (as MLP neural network) on the accuracy of forecasts are presented.