Prediction of Solar Energy from Sky Images and Meteorological Data Using Neural Networks

Many of the renewable energy generation methods suffer from the variable power output they produce. This is because they depend on elements with a substantial stochastic nature. To mitigate the variability different controlling mechanisms must be deployed. For thorough and optimal control, it is a necessity to predict the generated power in the near or more distant future. In this article, we explore the possibility of predicting electricity production by photovoltaic panels based on sky images. In addition, we also tested selected meteorological data that are relevant and available. For this purpose, we used SkyCam dataset that contains required information. We have proposed 3 scenarios combining block-based and sequential processing of sky images and selected meteorological data using CNN and LSTM networks. The best results were obtained by a combination of sky images and meteorological data processed as sequence of frames by LSTM. The achieved mean absolute error was 44.3 W/m
²
for 15-minute intervals.