Elektrik akıllı şebeke stabilitesi için yapay zeka modellerinin uygulanması

This study does a comparative analysis of several models, including XGBoost, SVM, Random Forest, KNN, Logistic Regression, Decision Tree, Neural Network, SimpleRNN, LSTM, and GRU. The performance of these models is evaluated based on metrics such as accuracy, precision, recall, and F1-score. The XGBoost Classifier is considered the optimal model due to its superior combination of precision, computational effectiveness, and interpretability. The findings presented in this study underscore the considerable capacity of machine learning in facilitating predictive analytics within the context of smart grids. Moreover, these results establish a robust basis for further research endeavours in this domain. Nevertheless, the literature highlights some challenges, such as the intricate nature of models, their limited interpretability, and the substantial computational demands they impose. These issues underscore the necessity for more research and enhancements in this field. As the study concludes, this paper offers strategic recommendations for effectively integrating the findings into actual applications of smart grids. Additionally, it outlines potential avenues for future research in this field.