Forecasting Smart Grid Stability Using Machine Learning Models

This paper presents a comprehensive study on designing and evaluating machine learning models for forecasting smart power grid stability. The stability of power grids is crucial for balancing electricity supply and demand, but it is challenged by the increasing use of renewable resources and dynamic pricing. Concepts like Decentralized Smart Grid Control (DSGC) aim to address this issue. We developed and compared machine learning models, including Support Vector Machines (SVM), LightGBM, and deep learning architectures, to identify the most accurate approach for forecasting grid stability. Our results showed that the SVM model achieved the highest accuracy of 99.9%, outperforming other models. These outcomes provide valuable insights and practical recommendations for deploying effective forecasting models in real-world smart grid scenarios, ensuring stability, efficient energy management, and seamless integration of renewable resources.