Solar Irradiance Forecasting Approaches Based on Machine Learning: A Systematic Literature Review

Abstract: The prediction of solar irradiance plays a crucial role in the design, performance, and stability of renewable energy sources, and especially photovoltaic (PV) power generation. Accurate forecasting helps in managing energy, grid stability, and integration of solar energy in contemporary power systems. The study is a Systematic Literature Review (SLR) of 37 recent (2019-2025) peer-reviewed papers on solar irradiance forecasting that apply Machine Learning (ML), Deep Learning (DL), and hybrid or ensemble modelling methods. The review is based on the Preferred Reporting Items of Systematic Reviews and Meta-Analyses (PRISMA 2020) to make it transparent and reproducible. A thorough search of seven large databases, such as Google Scholar, IEEE Xplore, Web of Science, Springer Nature Link, ScienceDirect, MDPI and the ACM Digital Library, was conducted to find relevant studies. Based on a structured synthesis of the chosen literature, the findings suggest that there is a definite methodological change in the traditional ML methods to DL and hybrid modelling structures. Although the classical ML algorithms have low computational complexity and can be effectively used to make short-term predictions, DL architectures consistently outperform them in terms of capturing nonlinear temporal and spatial patterns in solar irradiance data. Moreover, hybrid models combining DL architectures with signal decomposition and feature fusion methods also improve predictive accuracy. Nevertheless, the review notes that there are a number of ongoing shortcomings, such as the lack of geographic generalizability because of single-site dominance, the lack of consistency in reporting computational efficiency, the inconsistency of evaluation metrics, the lack of robustness testing in dynamic weather conditions, and a strong bias towards short-term forecasting horizons. In order to fill these gaps, future studies need to focus on multi-site and cross-climatic validation, domain adaptation using transfer learning, designing lightweight models to deploy in real-time, standardised benchmarking guidelines, and broaden their scope to medium and long-term forecasting with enriched meteorological inputs. Overall, the results offer an evidence-based, systematic review of existing trends in methodology and emphasise the need to balance predictive accuracy with generalizability, efficiency, and practical application in solar energy forecasting systems.

Sempe Thom Leholo, Chunling Tu and Topside Ehleketani Mathonsi. “Solar Irradiance Forecasting Approaches Based on Machine Learning: A Systematic Literature Review”. International Journal of Advanced Computer Science and Applications (IJACSA) 17.4 (2026). http://dx.doi.org/10.14569/IJACSA.2026.0170447

@article{Leholo2026,
title = {Solar Irradiance Forecasting Approaches Based on Machine Learning: A Systematic Literature Review},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2026.0170447},
url = {http://dx.doi.org/10.14569/IJACSA.2026.0170447},
year = {2026},
publisher = {The Science and Information Organization},
volume = {17},
number = {4},
author = {Sempe Thom Leholo and Chunling Tu and Topside Ehleketani Mathonsi}
}