Abstract
In recent years, the advancement of renewable energy technologies has gained significant momentum, with wind energy emerging as a prominent source of clean electricity generation. The efficient operation and maintenance of wind turbines are crucial for maximizing energy production while minimizing costs. This paper presents the development of a machine learning (ML) model tailored specifically for wind turbine performance optimization. The proposed ML model integrates various data sources including meteorological conditions, turbine sensor data, historical performance records, and maintenance logs. Leveraging these datasets, the model employs sophisticated algorithms such as neural networks, decision trees, and ensemble methods to predict turbine performance, detect anomalies, and optimize maintenance schedules.