DOI: https://doi.org/10.61435/ijred.2026.62760
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Machine learning in solar energy systems: Methods, applications, and future directions

1Institute of Engineering, HUTECH University, Ho Chi Minh City, Viet Nam

2Department of Mechanical Engineering, Delhi Skill and Entrepreneurship University, Delhi, India

3Faculty of Engineering, Dong Nai Technology University, Dong Nai, Viet Nam

4 Hanoi Amsterdam High School for the Gifted, Hanoi, Viet Nam

5 Institute of Maritime, Ho Chi Minh City University of Transport, Ho Chi Minh City, Viet Nam

6 Logistics Center, Ho Chi Minh City University of Transport, Ho Chi Minh City, Viet Nam

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Received: 2 Mar 2026; Revised: 7 May 2026; Accepted: 20 May 2026; Available online: 3 Jun 2026; Published: 1 Jul 2026.
Editor(s): H Hadiyanto
Open Access Copyright (c) 2026 The Author(s). Published by Centre of Biomass and Renewable Energy (CBIORE)
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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Abstract

In the present era, the ever-growing need for energy and the greenhouse gas emissions from fossil fuel burning have become a real challenge. Solar energy is an attractive option among various options available in renewable energy domain.  Solar energy systems are rapidly expanding, and that growth brings real challenges as they need to face challenges such as unpredictable output, constant changes, and complex operations. To handle these challenges and for smoother operation, Machine Learning (ML) can be useful as it can handle a large amount of data and keep everything running smoothly. In this review, a comprehensive overview of applying ML to solar energy is presented. The review will explore the working of existing ML techniques, covering both conventional as well as modern approaches. The key application areas are identified, ranging from forecasting and optimization to fault detection and energy management in integrated grids. It also discusses some important barriers like data inconsistency, the black-box nature of conventional ML models, and the difficulty in scaling up to real-world settings. On the brighter side, the review points to some exciting new directions like explainable AI, physics-informed learning, and real-time analytics. It is observed that it is a rapidly evolving field with marked shifting toward ML tools that are more flexible, explainable, and can be tuned into the bigger system. Overall, this review provides a combined and forward-looking perspective, offering actionable insights for the development of robust, scalable, and practically deployable ML solutions in solar energy systems.

Keywords: Machine Learning; Solar Energy System; Optimization; Sustainability; Explainable Artificial Intelligence

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Section: Review Article
Language : EN
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