A Comprehensive Review of Machine Learning Techniques for Solar Energy Resource Assessment and Prediction

DOI

10.2991/978-94-6239-685-2_10How to use a DOI?

Keywords

Solar Energy; Machine Learning; Irradiance Prediction; Deep Learning; Resource Assessment; CNN-LSTM

Abstract

Solar energy resource assessment plays a critical role in optimizing photovoltaic (PV) deployment, forecasting energy output, and ensuring long-term renewable energy planning. Traditional statistical and physical models often struggle with variability in climate conditions, complex environmental interactions, and heterogeneous datasets. Machine Learning (ML) techniques have emerged as powerful tools capable of addressing these limitations by learning complex nonlinear relationships and integrating multisource data. This review paper presents a comprehensive study of machine learning approaches used for solar irradiance prediction, resource mapping, anomaly detection, and system optimization. It covers traditional ML models, deep-learning architectures such as RNN, GRU, and LSTM, hybrid CNN–LSTM models, ensemble techniques, and satellite-based ML frameworks. Key challenges, recent advancements, comparative model performance, and future research directions are also discussed.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Gopal Bhalani
AU  - Mukul Mistry
AU  - Helly Khambhatwala
AU  - Poojan Mardiya
AU  - Alok Kumar Singh
PY  - 2026
DA  - 2026/05/26
TI  - A Comprehensive Review of Machine Learning Techniques for Solar Energy Resource Assessment and Prediction
BT  - Proceedings of the International Conference on Infrastructure Development and Sustainability (ICIDS 2025)
PB  - Atlantis Press
SP  - 149
EP  - 162
SN  - 3005-155X
UR  - https://doi.org/10.2991/978-94-6239-685-2_10
DO  - 10.2991/978-94-6239-685-2_10
ID  - Bhalani2026
ER  -