As wind energy continues to establish itself as a cornerstone of the global shift toward renewable power, effectively managing its inherent variability and operational risks has become increasingly critical. Artificial Intelligence (AI) is emerging as a key enabler in this space, offering advanced capabilities for both accurate wind forecasting and comprehensive risk assessment. This paper specifically explores how AI can strengthen the reliability and resilience of wind energy systems by addressing challenges such as wind intermittency, mechanical failures, and extreme weather events.
AI-driven models, including deep neural networks (DNNs), recurrent neural networks (RNNs), and convolutional neural networks (CNNs), are now widely used for both short-term and long-term wind speed prediction. These forecasts support more efficient grid scheduling, market integration, and overall turbine performance. At the same time, AI-based tools for anomaly detection, reinforcement learning, and predictive maintenance help identify equipment issues in advance, enabling proactive responses and reducing unplanned downtime.
The paper places particular emphasis on real-world applications of these technologies—such as using CNNs to model wind behavior across time and space, applying reinforcement learning to dynamically adjust turbine loads, and developing hybrid AI systems that combine meteorological data with sensor inputs to assess specific risks at wind farm sites. These approaches are already showing significant promise in reducing operational costs and increasing the robustness of wind energy infrastructure.
Finally, the paper highlights the evolving potential of AI-powered decision support systems to transform wind energy operations—making them not only smarter and more efficient but also more adaptable to future challenges.