An efficient smart grid stability prediction system based on machine learning and deep learning fusion model

Abstract

A smart grid is a modern power system that allows for bidirectional communication, driven mostly by the idea of demand responsiveness. Predicting the stability of the smart grid is necessary for improving its dependability and maximizing the efficacy and regularity of electricity delivery. Predicting smart grid stability is difficult owing to the various elements that impact it, including consumer and producer engagement, which may contribute to smart grid stability. This research work proposes machine learning (ML) and deep learning (DL) approaches for predicting smart grid sustainability. Five ML algorithms, namely support vector machine (SVM), decision tree (DT), K-nearest neighbor (KNN), random forest (RF), and logistic regression (LR), were applied for the prediction of smart grid stability. Later, the stacking ensemble and voting ensemble of ML algorithms were also applied for prediction. To further increase accuracy, a novel fusion model with DL artifical neural networks (ANN) and ML SVM was applied and achieved an accuracy of 98.92%. The experiment results show that the proposed model outperformed existing models for smart grid stability prediction.