Harnessing artificial intelligence and machine learning for predictive maintenance and optimization in renewable energy systems: a mini-review
The integration of Artificial Intelligence (AI) and Machine Learning (ML) into renewable energy systems (RES) is increasingly recognized as a practical pathway for improving operational efficiency, reliability, and sustainability. Renewable sources, such as solar, wind, and hydropower, as well as hybrid configurations, are inherently intermittent and operationally complex, creating persistent challenges for grid stability, asset reliability, and energy optimization. Conventional maintenance strategies, whether reactive or preventive, often lead to unplanned downtime, inefficient inspections, and increased lifecycle costs. In response, AI/ML-enabled predictive maintenance (PdM) leverages high-frequency sensor data, SCADA streams, and historical performance records to detect anomalies, diagnose faults, and estimate remaining useful life (RUL), enabling proactive maintenance interventions. Beyond maintenance, AI/ML supports operational optimization through energy generation forecasting, load prediction, grid integration, storage scheduling, and adaptive control, thereby strengthening system resilience and lowering operational costs. Unlike prior reviews that treat PdM and RES optimization as separate topics, this work provides a unified, decision-oriented synthesis that explicitly links (i) maintenance outcomes (fault detection/diagnosis/RUL) and (ii) operational outcomes (forecasting, dispatch, storage scheduling, and grid control) through shared data pipelines and coupled decision trade-offs. The review further provides a structured mapping that connects AI/ML methods to (a) RES asset types (solar, wind, hydropower, hybrid, and emerging marine systems), (b) decision objectives (fault/RUL, forecasting, dispatch and control), and (c) deployment settings (IoT/SCADA, edge-cloud, and digital-twin-enabled monitoring). Emerging technologies, including digital twins, edge AI, federated learning, and explainable AI (XAI), are discussed as enabling mechanisms for real-time monitoring, privacy-preserving learning, adaptive decision-making, and transparency in critical infrastructure. Cybersecurity risks including vulnerabilities arising from expanded IoT/edge/cloud connectivity and adversarial threats to AI-driven control are highlighted as a critical adoption barrier alongside data quality, interoperability with legacy systems, scalability, and model interpretability. By consolidating fragmented evidence across maintenance and optimization and highlighting deployment trade-offs, this review provides an implementation-oriented reference for AI/ML-enabled operation of modern renewable energy systems.