Forecasting solar power generated by grid connected PV systems using ensembles of neural networks

Abstract

Forecasting solar power generated from photovoltaic systems at different time intervals is necessary for ensuring reliable and economic operation of the electricity grid. In this paper, we study the application of neural networks for predicting the next day photovoltaic power outputs in 30 minutes intervals from the previous values, without using any exogenous data. We propose three different approaches based on ensembles of neural networks - two non-iterative and one iterative. We evaluate the performance of these approaches using four Australian solar datasets for one year. This includes assessing predictive accuracy, evaluating the benefit of using an ensemble, and comparing performance with two persistence models used as baselines and a prediction model based on support vector regression. The results show that among the three proposed approaches, the iterative approach was the most accurate and it also outperformed all other methods used for comparison.