Predictive Risk Identification Models in Renewable Energy Infrastructure Projects: Mitigating Climate Variability and Equipment Degradation Risks

• Abbas Ahsun

  Texas University

  Author

The accelerating global energy transition has precipitated large-scale deployment of renewable energy infrastructure, yet these assets face unprecedented operational risks from climate variability and accelerated equipment degradation. Traditional risk assessment methodologies, reliant on periodic inspections and static historical failure data, prove inadequate for capturing the complex, non-linear interactions between environmental stressors and component-level degradation. This study develops and validates a predictive risk identification framework integrating a hybrid CNN-LSTM-Attention deep learning architecture with Space-Air-Ground (SAG) cooperative observation data. The proposed model, trained on operational and meteorological data from solar photovoltaic plants, offshore wind turbines, and hybrid renewable energy systems spanning 2018–2025, achieves an accuracy of 89.4% in predicting high-risk events with a lead time of 48–72 hours. The Differential Significance Criterion (DSC)-enhanced Physics-Informed Neural Network (PINN) component improves interpretability while maintaining predictive fidelity. The framework outperforms conventional static budget-based risk assessment methods, demonstrating statistically significant improvements in early warning capability (p < 0.01) and reduction in false alarm rates (from 22.3% to 8.1%). This research provides a replicable methodological foundation for transitioning renewable energy asset management from reactive maintenance paradigms to predictive, risk-informed decision-making, with implications for infrastructure resilience, operational cost reduction, and sustainable energy system reliability.

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Copyright (c) 2026 Abbas Ahsun (Author)

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