Smart Wind Resource Assessment Using Big Data: Architecture, Tools, and Future Directions

초록

As the global demand for renewable energy accelerates, wind power has emerged as a vibrant contributor to sustainable energy generation. Accurate and efficient wind resource assessment is critical for optimizing site selection, turbine performance, and overall energy output. This review presents a comprehensive and futuristic big data framework designed specifically for smart wind resource assessment. The proposed framework integrates advanced data banking systems and big data analytics to streamline the collection, processing, integration, and storage of large-scale wind-related datasets. Key features include a centralized and scalable data repository, high-resolution temporal and spatial data acquisition, and the application of machine learning (ML) and data visualization techniques to enhance decision-making processes. The study explores how traditional wind assessment methods can be augmented using big data tools to provide deeper insights into wind behavior, resource availability, and site feasibility. In addition, the framework supports real-time monitoring and predictive modeling, enabling proactive energy management and operational optimization. Several practical applications and case studies are discussed, highlighting the framework's effectiveness in improving wind farm planning, reducing environmental impact, and supporting policy formulation. This review aims to guide researchers, industry professionals, and policymakers in adopting data-driven approaches for next-generation wind energy systems.

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