THE ROLE OF DATA SCIENCE IN OPTIMIZING RENEWABLE ENERGY GENERATION FROM WIND FARMS: A CRITICAL CONCEPTUAL REVIEW
Keywords:
Data science, Wind energy, Machine learning, Predictive maintenance, Power forecasting, Renewable energy optimizationAbstract
This study systematically reviews the role of data science in optimizing renewable energy generation from wind farms, addressing the growing demand for efficient, reliable, and cost-effective wind energy systems in the global transition to low-carbon energy. Despite its maturity and scalability, wind energy continues to face challenges related to resource variability, forecasting uncertainty, operational inefficiencies, and high maintenance costs, necessitating advanced optimization strategies. The primary objective of this review is to examine how data-driven techniques enhance operational efficiency, system reliability, and energy output in both onshore and offshore wind farms. The study follows the PRISMA 2020 guidelines, with peer-reviewed literature sourced from Scopus, Web of Science, IEEE Xplore, and Science Direct. Studies were selected using predefined inclusion and exclusion criteria, and data were extracted on applied methods, datasets, optimization objectives, and performance metrics. A qualitative synthesis, supported by comparative performance analysis where feasible, was conducted. The findings show widespread application of machine learning, deep learning, and hybrid models, particularly in wind power forecasting, turbine performance optimization, predictive maintenance, and grid integration. Across the reviewed studies, data science techniques consistently improve forecasting accuracy, turbine efficiency, and maintenance effectiveness, resulting in reduced downtime and operational costs. Overall, data-driven approaches outperform many traditional methods in managing the complexity and variability of wind energy systems. In conclusion, the review establishes data science as a key enabler of efficient, reliable, and economically viable wind farm operations, while emphasizing the need for future research on explainable models, standardized benchmarks, and scalable, integrated optimization frameworks.
