Wind Power Generation and Wind Energy Utilization

Dear Colleagues,

Wind energy plays an increasingly significant role in the global transition toward cleaner and more resilient power systems. Recent advances in data science—encompassing big data analytics, machine learning (ML), and artificial intelligence (AI)—have opened new avenues for improving the efficiency, reliability, and economic viability of wind power generation. By extracting actionable insights from vast operational datasets, researchers and industry practitioners can develop more accurate resource assessments, optimize turbine performance, and reduce maintenance costs. These data-centric techniques also facilitate the creation of predictive models that anticipate faults or component degradation, thus enabling proactive interventions that minimize downtime and extend the service life of wind turbines.

At the same time, the complex nature of wind resources and the growing scale of wind farms call for robust forecasting tools and advanced control algorithms. Accurate wind speed and power output predictions are essential for ensuring grid stability, scheduling maintenance, and informing market operations. By integrating SCADA (Supervisory Control and Data Acquisition) data, meteorological information, and real-time sensor readings, researchers are making significant strides in short-term forecasting and long-term resource planning. In addition, high-fidelity digital twins, which replicate the physical and operational characteristics of turbines in a virtual environment, support real-time monitoring, fault diagnosis, and scenario testing. These tools not only improve operational decision-making, but also contribute to more effective policy and regulatory frameworks for large-scale wind deployments.

While data science occupies a central position in this Special Issue, hardware-oriented research remains critical to advancing wind energy technology. For instance, the application of novel sensor systems and advanced materials in blade manufacturing can yield more reliable performance data and higher energy capture. Insights from data analytics can then guide design improvements, inform structural health monitoring, and enhance overall turbine resilience. Ultimately, the convergence of data science with hardware innovations will shape next-generation wind energy systems that are both adaptive and cost-effective.

This Special Issue aims to present state-of-the-art research on data-driven solutions and integrated approaches to wind power generation and utilization. We invite contributions that address novel analytical methods, specialized hardware applications, case studies, and reviews. Topics of interest include, but are not limited to, the following:

• Data analytics, machine learning, and artificial intelligence for wind energy;
• Short-term and long-term wind forecasting methods;
• Predictive maintenance and fault diagnosis in wind turbines;
• Digital twin technologies and real-time turbine modeling;
• Advanced sensor systems and their integration with data-driven strategies;
• SCADA data fusion and big data management;
• Grid stability, power balancing, and system-level optimization;
• Socio-economic and policy implications of data-centric wind energy solutions.

We look forward to receiving your submissions and believe that this Special Issue will contribute significantly to the evolution of wind energy research and practice.

Prof. Dr. Yingying Zheng
Dr. Yongning Zhao
Guest Editors

Manuscript Submission Information

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• wind energy analytics
• predictive maintenance
• machine learning
• digital twin
• advanced sensor systems
• SCADA data integration
• structural health monitoring
• grid stability