REVIEW OF MACHINE LEARNING APPLICATIONS FOR ANALYSIS AND CONTROL OF ENERGY SYSTEMS

Barannik Vladislav Andreevich

The article explores AI and machine learning applications for energy system analysis and control, focusing on integrating these technologies into automated process control systems to enhance efficiency, stability and security. It examines key approaches including mathematical optimization models, SCADA-based load forecasting, and spectral analysis for power grid stability assessment. The study highlights explainable AI techniques to improve decision transparency and proposes an innovative SDN-RNN framework for real-time SCADA cyberattack detection. Practical implementations demonstrate how ONNX-based machine learning integration can automate processes and prevent failures. The findings show AI's potential to transform energy infrastructure management while addressing modern technological challenges.

Keywords:Artificial intelligence, machine learning, mathematical modeling, energy systems, SCADA, industrial control systems (ICS), ONNX, dispatching.