Towards Sustainable Energy Grids: A Machine Learning-Based Ensemble Methods Approach for Outages Estimation in Extreme Weather Events

Abstract

The critical challenge of enhancing the resilience and sustainability of energy management systems has arisen due to historical outages. A potentially effective strategy for addressing outages in energy grids involves preparing for future failures resulting from line vulnerability or grid disruptions. As a result, many researchers have undertaken investigations to develop machine learning-based methodologies for outage forecasting for smart grids. This research paper proposed applying ensemble methods to forecast the conditions of smart grid devices during extreme weather events to enhance the resilience of energy grids. In this study, we evaluate the efficacy of five machine learning algorithms, namely support vector machines (SVM), artificial neural networks (ANN), logistic regression (LR), decision tree (DT), and Naive Bayes (NB), by utilizing the bagging ensemble technique. The results demonstrate a remarkable accuracy rate of 99.98%, with a true positive rate of 99.6% and a false positive rate of 0.01%. This research establishes a foundation for implementing sustainable energy integration into electrical networks by accurately predicting the occurrence of damaged components in the energy grid caused by extreme weather events. Moreover, it enables operators to manage the energy generated effectively and facilitates the achievement of energy production efficiency. Our research contributes to energy management systems using ensemble methods to predict grid vulnerabilities. This advancement lays the foundation for developing resilient and dependable energy infrastructure capable of withstanding unfavorable weather conditions and assisting in achieving energy production efficiency goals.