Review of Data-Driven Approaches Applied to Time-Series Solar Irradiance Forecasting for Future Energy Networks

Xuan Jiao and Weidong Xiao ()
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Xuan Jiao: School of Electrical and Computer Engineering, University of Sydney, Sydney, NSW 2006, Australia
Weidong Xiao: School of Electrical and Computer Engineering, University of Sydney, Sydney, NSW 2006, Australia

Energies, 2025, vol. 18, issue 21, 1-22

Abstract: The fast-increasing penetration of photovoltaic (PV) power raises the issue of grid stability due to its intermittency and lack of inertia in power systems. Solar irradiance forecasting effectively supports advanced control, mitigates power intermittency, and improves grid resilience. Irradiance forecasting based on data-driven methods aims to predict the direction and level of power variation and indicate quick action. This article presents a comprehensive review and comparative analysis of data-driven approaches for time-series solar irradiance forecasting. It systematically evaluates nineteen representative models spanning from traditional statistical methods to state-of-the-art deep learning architectures across multiple performance dimensions that are critical for practical deployment. The analysis aims to provide actionable insights for researchers and practitioners when selecting and implementing suitable forecasting solutions for diverse solar energy applications.

Keywords: photovoltaic power systems; solar forecasting; data-driven prediction; statistical models; machine learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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