Improving irradiance prediction by using classification of sky images and deep learning

Abstract

Short-term solar forecasting is essential for the large-scale application of solar energy and is necessary for the operation of power plants, energy trading, and grid balancing. The main cause of uncertainty in solar forecasting is cloud movement, which can be observed by All-Sky Images. The spatial layout and temporal dynamics of clouds cannot be extracted by conventional cloud modelling approaches utilising image analysis techniques, leading to inaccurate predictions of the interaction with solar radiation. In this study an categorization method is made based on sky conditions and from that five classes are made. The goal of this classification method is to improve the 21-minute irradiance predictions which are made with a deep learning model. The outcome of the 21-minute deep learning model will get compared with the Persistence, Smart Persistence and ARIMA model. This classification method and irradiance prediction are applied for location Folsom, California and Delft, Netherlands. The irradiance predictions based on sky classification showed an improvement of 8.6% for location Delft and 29.3% for location Folsom when the same dataset sizes are used. The irradiance predictions were also done for various dataset sizes and compared per location. Finally, it provides a way forward to improve the classification method and deep learning models to anticipate short-term irradiance in the future.