OPTIMIZATION OF RENEWABLE ENERGY SYSTEMS USING MACHINE LEARNING ALGORITHMS

The article analyzes optimization of renewable energy systems using machine learning algorithms. Machine learning improves renewable energy system performance by forecasting energy production, optimizing storage, and balancing variable generation. Intelligent algorithms support more stable integration of solar and wind resources into modern power systems. The aim of the study was to evaluate the technical, functional, and practical significance of this approach in modern engineering systems. The study used analytical review, comparative assessment, and synthesis of current engineering literature. The results show that the investigated technology improves operational efficiency, reliability, safety, and sustainability. The findings may be useful for engineers, researchers, and managers involved in the modernization of industrial and technological processes.