<p>Smart grid is an
essential concept in the transformation of the electricity sector into an
intelligent digitalized energy network that can deliver optimal energy from the
source to the consumers. Smart grids being self-sufficient systems are
constructed through the integration of information, telecommunication, and
advanced power technologies with the existing electricity systems. Artificial
Intelligence (AI) is an important technology driver in smart grids. The application of AI techniques in smart
grid is becoming more apparent because the traditional modelling optimization
and control techniques have their own limitations. Machine Learning (ML) being
a sub-set of AI enables intelligent decision-making and response to sudden
changes in the customer energy demands, unexpected disruption of power supply,
sudden variations in renewable energy output or any other catastrophic events
in a smart grid. This paper presents the comparison among some of the
state-of-the-art ML algorithms for predicting smart grid stability. The dataset
that has been selected contains results from simulations of smart grid
stability. Enhanced ML algorithms such as Support Vector Machine (SVM), Logistic
Regression, K-Nearest Neighbour (KNN), Naïve Bayes (NB), Decision Tree (DT),
Random Forest (RF), Stochastic Gradient Descent (SGD) classifier, XGBoost and
Gradient Boosting classifiers have been implemented to forecast smart grid
stability. A comparative analysis among the different ML models has been
performed based on the following evaluation metrics such as accuracy,
precision, recall, F1-score, AUC-ROC, and AUC-PR curves. The test results that
have been obtained have been quite promising with the XGBoost classifier
outperforming all the other models with an accuracy of 97.5%, recall of 98.4%,
precision of 97.6%, F1-score of 97.9%, AUC-ROC of 99.8% and AUC-PR of 99.9%. </p>
A Comparative Analysis of Enhanced Machine Learning Algorithms for Smart Grid Stability Prediction
