Machine Learning for Solar Radiation and Photovoltaic Systems: Evidence, Validation, and Operational Applications

Machine learning is used in solar-energy forecasting and management, but reported gains depend on the task, data and evaluation design. This review combines descriptive Scopus bibliometric mapping with a critical narrative synthesis of applications in irradiance estimation, photovoltaic power prediction, fault diagnosis, energy management and grid integration. The photovoltaic search yielded 1,954 unique records published between 2010 and 2026; a separate solar-energy search returned 6,767 results. Their VOSviewer maps are interpreted independently. We examine technical studies by prediction target, forecast horizon, input data, climate, validation method, benchmark and operational purpose. Results from different sites, targets and test designs cannot establish a universal ranking of model families. Classical, ensemble and deep-learning methods each have useful applications, but their reported accuracy should be interpreted against appropriate baselines and leakage-free temporal tests. External-site evaluation and calibrated uncertainty are essential when forecasts inform operational decisions. Physics-guided approaches may improve consistency, whereas digital twins, graph learning, federated learning and autonomous control require more task-specific validation. The review identifies transparent search reporting, reproducible data processing and cross-climate testing as priorities. Because the complete search history and technical screening records are unavailable, the synthesis is descriptive rather than systematic, and bibliometric counts do not measure model quality.