Advancing solar PV power plants: The role of artificial intelligence in optimization, sustainability, and cost efficiency

With a focus on data-driven monitoring, measurement, and intelligent control frameworks, this study explores the role of artificial intelligence (AI) in the digital transformation of solar photovoltaic (PV) power plants. AI and machine learning (ML) approaches are becoming essential tools for deriving actionable insights from high-dimensional operational data as PV systems become more sensorized and networked. With an emphasis on their integration with digital measurement infrastructures and real-time data collecting systems, this paper examines AI-based methods for solar power forecasting, tracking maximum power points, fault detection and diagnostics, predictive maintenance, and hybrid system optimization. Technical limitations, such as sensitivity to data quality, idea drift under changing environmental conditions, transferability constraints, and computer scalability, are critically assessed, and reported performance metrics from previous studies are explored in comparison. The contribution of digital twins, adaptive data analytics, and IoT-enabled sensing to improving observability, dependability, and decision support in PV plants is highlighted in particular. Standardized validation procedures and extensive real-world benchmarking are still required, despite the fact that a number of studies show encouraging gains in accuracy and operating efficiency. This analysis offers a fair and technically sound viewpoint on the potential and limitations of intelligent solar PV management in increasingly data-centric energy infrastructures by combining developments in AI-driven digitalization and measurement systems.