Optimizing IoT-driven smart grid stability prediction with dipper throated optimization algorithm for gradient boosting hyperparameters

With the surge in global population and economic expansion, there's been a marked increase in electricity demand. This necessitates the efficient distribution of electricity to both residential and industrial sectors to minimize energy loss. Smart Grids (SG) emerge as a promising solution to reduce power dissipation in distribution networks. The application of machine learning and artificial intelligence in SGs has significantly improved the precision of predicting consumer electricity needs. This paper presents a novel approach to improving the stability prediction of Internet of Things (IOT)-driven SGs using different advanced machine learning models. This study explores multiple advanced machine-learning techniques, including Gradient Boosting (GB), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Neural Networks, and the Decision Tree classifier, focusing on the stability prediction of SGs. This study explores the efficiency of hyperparameter-optimized GB models in predicting SG dynamic stability that encompasses the ability of the system to return to a stable operating point following a disturbance. Focusing on various models, it identifies the Dipper Throated Optimization Algorithm DTO+GB model as the standout, exhibiting unparalleled accuracy and reliability across critical performance metrics such as accuracy (99.32 %), sensitivity (99.16 %), and specificity (99.54 %). Diagnostic and regression analyses further emphasize its better predictive power and the need for hyperparameter optimization to improve the model. This paper highlights the capabilities of advanced machine learning algorithms in conjunction with tactical hyperparameter optimization in enhancing SG stability prediction, introducing a new baseline for future technological and methodological developments in this application.

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