Leveraging AI for Enhanced Solar Energy Efficiency and Prediction
Keywords:
Artificial intelligence, solar, energy, IoT.Abstract
As solar photovoltaics (PV) have emerged as the most economical source of electricity globally, improving the efficiency, reliability, and scalability of PV power plants has become increasingly critical. Traditional performance evaluation methods, which rely on isolated and plant-specific assessments, are inadequate for managing the operational complexity of large-scale and rapidly expanding solar infrastructures. Consequently, intelligent and automated approaches are required to enhance system performance and reduce efficiency losses.
Artificial intelligence (AI) provides a powerful framework for data-driven optimization of PV modules and plants by leveraging advanced machine learning and deep learning techniques. AI-based models enable accurate solar power generation forecasting through the integration of historical output data, real-time sensor measurements, and meteorological parameters. Additionally, intelligent diagnostics support early fault detection, reliability assessment, and root-cause analysis of performance degradation caused by soiling, shading, thermal stress, inverter faults, and module aging. Explainable AI further enhances decision-making by improving model transparency and trustworthiness in operational environments.
By enabling predictive maintenance and adaptive control strategies, AI-driven PV systems significantly improve energy yield, operational resilience, and asset longevity. This study investigates AI-enabled methodologies for performance enhancement, output forecasting, and degradation analysis in photovoltaic power plants, highlighting their essential role in advancing efficient, autonomous, and sustainable solar energy systems for future smart grid applications.
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