SMART GRID STABILITY PREDICTION AND EVALUATION USING MODERN MACHINE LEARNING ALGORITHMS

Abstract

The global demand for energy is rapidly rising. As a result, energy systems must evolve and be upgraded in order to become more efficient, adaptable, and sustainable. A smart grid reduces workforce while providing consumers with safe, reliable, high-quality, and long-lasting electricity. Smart grids use digital communication technology to enable two-way flow of electricity and data. This vast amount of data needs to be processed for making better decisions for maintaining the grid stability. ML and AI approaches are utilised to acquire, store, and manage this data. This study compares the performance of modern machine learning methods for predicting smart grid stability. The dataset that was chosen contains findings from a smart grid simulation. XGBoost, Adaboost, Gradient Boosting Method (GBM), HistGBM, LightGBM and CatBoost algorithms have been implemented to forecast smart grid stability. Performance of the ML model has been evaluated based on Classification evaluation metrics. The following evaluation metrics such as accuracy, precision, recall, F1-score, MCC, specificity, training time, predicting time, AUC-ROC curve and AUC-PR CURVE are used for classification evaluation.An efficient Stacking Ensemble Classifier (SEC) model developed by using the above mentioned machine learning algorithms and compared the evaluated results of individual machine learning models with the SEC model.