Big data solar power forecasting based on deep learning and multiple data sources

In this paper, we consider the task of predicting the electricity power generated by photovoltaic solar systems for the next day at half-hourly intervals. We introduce DL, a deep learning approach based on feed-forward neural networks for big data time series, which decomposes the forecasting problem into several sub-problems. We conduct a comprehensive evaluation using 2 years of Australian solar data, evaluating accuracy and training time, and comparing the performance of DL with two other advanced methods based on neural networks and pattern sequence similarity. We investigate the use of multiple data sources (solar power and weather data for the previous days, and weather forecast for the next day) and also study the effect of different historical window sizes. The results show that DL produces competitive accuracy results and scales well, and is thus a highly suitable method for big data environments.