(untitled) (Comprehensive Journal of Science, 2025)
Ensuring the reliability and efficiency of power plants in Libya requires advanced predictive maintenance strategies. Traditional reactive and preventive approaches often fail to prevent unplanned downtime and inefficiencies in modern power systems. This study applies machine learning (ML) techniques, including K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Random Forest, to sensor and historical operational data for predicting maintenance requirements. Comparative evaluation demonstrates that Random Forest achieves superior accuracy (100%) and robustness, while SVM attains high accuracy (93.7%) but shows minor limitations in distinguishing normal and abnormal operational states. The results indicate that ML-based predictive maintenance can significantly reduce unexpected downtime, enhance operational efficiency, and optimize maintenance planning, offering a data-driven alternative to conventional approaches.