Aspects of Machine Learning applications in Power Systems

Abstract

Machine learning (ML) is revolutionizing the power industry by enabling data-driven solutions to a variety of complex challenges, from grid management to equipment maintenance. The integration of ML, especially within the framework of smart grids, enhances the efficiency, reliability, and security of power systems. Traditional power systems often rely on fixed, rule-based models that struggle to adapt to the increasing complexity introduced by renewable energy sources, distributed generation, and dynamic consumer demands. ML's ability to identify patterns in vast datasets makes it an ideal tool for addressing these issues. Accurate load forecasting is crucial for the efficient operation of power systems. It helps utilities predict future electricity consumption to ensure supply meets demand, preventing both power outages and the wasteful over-generation of energy. Machine learning excels at this task by analyzing historical load data alongside various influencing factors like weather patterns, economic indicators, and special events. Short-term forecasting (hours to days ahead) is vital for real-time grid operation, generator scheduling, and managing day-to-day energy distribution. ML models like recurrent neural networks (RNNs) and Long Short-Term Memory (LSTM) networks are particularly effective due to their ability to process sequential data and capture complex temporal relationships. Medium- and long-term forecasting (weeks to years ahead) is used for strategic planning, such as scheduling maintenance for power plants and planning for future infrastructure investments. Regression models and artificial neural networks (ANNs) are commonly employed for these longer time horizons..