Machine Learning Approaches for Predictive Maintenance in PV and Wind Energy Systems

Abstract - This study examines the incorporation of machine learning (ML) methodologies in predictive maintenance for photovoltaic (PV) and wind energy systems, with the objective of enhancing system reliability, reducing downtime, and optimizing performance. As renewable energy systems, especially photovoltaic and wind turbines, proliferate, the demand for effective maintenance solutions increases. Predictive maintenance, driven by machine learning, provides an effective solution by detecting probable problems and failures prior to their occurrence, facilitating prompt interventions. This research examines multiple machines learning methodologies, encompassing supervised learning, unsupervised learning, and deep learning, along with their applications in the assessment of photovoltaic and wind energy systems' health. The research also analyses the categories of data utilized for examination, including sensor data, operational parameters, and environmental elements. Challenges including data quality, model precision, and the necessity for extensive datasets are emphasized, along with prospects for future progress. This study underscores the significance of machine learning in improving the sustainability and operational efficiency of renewable energy systems. Key Words: Predictive maintenance, machine learning, solar systems, wind energy, renewable energy systems.