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Keywords

Artificial intelligence, Predictive maintenance, Renewable energy, Wind turbines, Solar farms, Energy storage systems, Machine learning

Document Type

Article

Abstract

The integration of Artificial Intelligence (AI) into predictive maintenance for renewable energy assets represents a significant advancement in modern energy management. This study reviews and analyzes AI applications in predictive maintenance across wind turbines, solar farms, and energy storage systems. It emphasizes the role of machine learning and deep learning algorithms in predicting failures before they occur, enhancing operational efficiency, and reducing maintenance costs. The paper also discusses challenges related to data quality, scalability, and system integration within renewable energy environments. Findings indicate that AI-based predictive maintenance significantly improves equipment reliability and system performance, paving the way toward smarter, more sustainable, and cost-effective energy infrastructures. Purpose: This systematic review critically analyzes the integration of Artificial Intelligence (AI) for predictive maintenance in renewable energy assets... Methodology: Following PRISMA guidelines, this study synthesizes findings from [Number] peer-reviewed articles published between 2018–2024... Findings: The synthesis indicates that while Deep Learning models (e.g., LSTM) demonstrate superior accuracy in fault forecasting, their deployment is often hindered by... Originality: Unlike previous reviews, this paper provides a critical comparison of algorithmic performance and highlights the gap between simulated results and industrial applicability.

DOI

10.65645/3105-9104.1027

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