Reliable prediction of solar power is very important to increase the penetration of this renewable energy source into the electricity grid. In this paper, we consider the task of forecasting solar power output from Photovoltaic (PV) systems at half-hourly intervals. We propose a new approach based on recurrent neural networks trained with cooperative neuro-evolution algorithm. We develop both univariate model which uses only previous power data and multivariate model which uses both previous power and weather data for prediction. We conduct a comprehensive evaluation of the proposed approach using 2 years of solar power data from a grid-connected PV plant. Evaluation shows that proposed approach achieves promising accuracy outperforming the persistence models used as baselines.
['Mashud Rana', 'Rohitash Chandra', 'Vassilios G. Agelidis']
Cooperative neuro-evolutionary recurrent neural networks for solar power prediction