Short-term solar irradiance forecasting in streaming with deep learning

Abstract

Solar energy is one of the most common and promising sources of renewable energy. In photovoltaic (PV) systems, operators can benefit from future solar irradiance predictions for efficient load balancing and grid stability. Therefore, short-term solar irradiance forecasting plays a crucial role in the transition to renewable energy. Modern PV grids collect large volumes of data that provide valuable information for forecasting models. Although the nature of these data presents an ideal setting for online learning methodologies, research to date has mainly focused on offline approaches. Hence, this work proposes a novel data streaming method for real-time solar irradiance forecasting on days with variable weather conditions and cloud coverage. Our method operates under an asynchronous dual-pipeline framework using deep learning models. For the experimental study, two datasets from a Canadian PV solar plant have been simulated as streams at different data frequencies. The experiments involve an exhaustive parameter grid search to evaluate four state-of-the-art deep learning architectures: multilayer percep tron (MLP), long-short term memory network (LSTM), convolutional network (CNN), and Transformer network. The obtained results demonstrate the suitability of deep learning models for this problem. In particular, MLP and CNN achieved the best accuracy, with a high capacity to adapt to the evolving data stream