Smart Wind Farm Management Using IoT and Predictive in Analytics

Abstract

The integration of Internet of Things (IoT) technology with predictive analytics is revolutionizing wind farm management by enabling real-time monitoring, efficient maintenance, and performance optimization. This study presents a comprehensive framework for smart wind farm management, leveraging sensor networks, cloud computing, and machine learning models to predict equipment failures, optimize power output, and reduce operational costs. By collecting and analyzing data from turbines, weather stations, and grid systems, the proposed approach facilitates data-driven decision-making for wind energy operators. Furthermore, predictive analytics is employed to forecast wind patterns and turbine health, improving energy efficiency and minimizing downtime. Experimental validation using real-world datasets demonstrates significant improvements in reliability and resource allocation. The research contributes to the development of sustainable and intelligent energy systems, aligning with global decarbonization goals and the transition to Industry 4.0.