A review of solar and wind energy forecasting: From single-site to multi-site paradigm

Alessio Verdone, Massimo Panella, Enrico De Santis and Antonello Rizzi

Applied Energy, 2025, vol. 392, issue C, No S0306261925007469

Abstract: The energy sector is undergoing a radical transformation towards an electricity generation system composed mainly of renewable energy sources. Although they offer several advantages compared to fossil fuels (such as scarcity and import-dependency), their stochastic nature makes them unreliable without an adequate storage system. Since electrical networks are characterized by complex dynamics, numerous methods for predicting renewable electricity production have been developed over the years. Among them, machine learning and deep learning methods in this field can be considered successful tools. In this review, we offer an overview of the methodologies applied to the task of renewable energy source forecasting, focusing on solar and wind. We classified methods depending on the number of sites and spatio-temporal information involved in the prediction. Recent research has demonstrated how the processing of simultaneous information coming from multiple plants allows the predictive system to take advantage of both temporal and spatial knowledge of the plants generating the related time series. Moreover, we have analyzed in detail the datasets employed in these experiments, to offer a clear and unified view of the experimental setups and the difficulty in producing a benchmark to compare methods. The purpose of this review is to offer the reader an updated view of the most modern renewable energy forecasting systems by comparing methodologies and approaches used in state-of-the-art research and providing a critical analysis of them.

Keywords: Energy transition; Machine learning; Deep learning; Renewable energy forecasting; Spatio-temporal learning; Multi-site forecasting (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.apenergy.2025.126016

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