MACHINE LEARNING–DRIVEN PREDICTIVE MAINTENANCE IN RENEWABLE ENERGY FACILITIES: AN END-TO-END FRAMEWORK
Keywords:
Predictive maintenance; Renewable energy; Machine learning; IoT; Anomaly detection; RUL estimation; Edge computing; Explainable AIAbstract
Predictive maintenance (PdM) powered by machine learning (ML) has
emerged as a vital tool in extending equipment life, reducing unplanned downtime, and
lowering operational costs in distributed renewable energy systems—particularly wind and
solar installations. This paper presents a comprehensive IMRaD-structured framework that
integrates IoT data acquisition, preprocessing, ML models (anomaly detection, fault
classification, RUL estimation), and scalable deployment strategies across edge/cloud
environments. Using referenced case studies and results from existing literature, we
demonstrate the efficacy of LSTM, Random Forest, and autoencoder models in achieving
up to 95% accuracy for fault detection and reducing maintenance costs by ≈30–50%. We
also discuss challenges such as data quality, interpretability, and cybersecurity, before exploring future directions including explainable AI (XAI), federated learning, and edge
based digital-twin solutions.
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