Machine learning in solar energy systems: Methods, applications, and future directions - Hoang Dat Do; Raghav Kumar Thakur; Xuan Manh Dinh; Do Duong Lam Le; Minh Thai Vu; Van Quy Nguyen; Ngoc Doanh Le (Center of Biomass & Renewable Energy (CBIORE), 2026)
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.