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Showing papers in "International Transactions on Electrical Energy Systems in 2021"









Journal ArticleDOI
TL;DR: In this article, state-of-the-art machine learning algorithms, namely Support Vector Machines (SVM), KNN, Logistic Regression, Naive Bayes, Neural Networks, and Decision Tree classifier, have been deployed for predicting the stability of the smart grid.
Abstract: The global demand for electricity has visualized high growth with the rapid growth in population and economy. It thus becomes necessary to efficiently distribute electricity to households and industries in order to reduce power loss. Smart Grids (SG) have the potential to reduce such power losses during power distribution. Machine learning and artificial intelligence techniques have been successfully implemented on SGs to achieve enhanced accuracy in customer demand prediction. There exists a dire need to analyze and evaluate the various machine learning algorithms, thereby identify the most suitable one to be applied to SGs. In the present work, several state-of-the-art machine learning algorithms, namely Support Vector Machines (SVM), K-Nearest Neighbor (KNN), Logistic Regression, Naive Bayes, Neural Networks, and Decision Tree classifier, have been deployed for predicting the stability of the SG. The SG dataset used in the study is publicly available collected from UC Irvine (UCI) machine learning repository. The experimentation results highlighted the superiority of the Decision Tree classification algorithm, which outperformed the other state of the art algorithms yielding 100% precision, 99.9% recall, 100% F1 score, and 99.96% accuracy.

41 citations




















Journal ArticleDOI
TL;DR: This paper aims to demonstrate the efforts towards in-situ applicability of EMMARM, as to provide real-time information about the physical properties of E-modulus and their applications in the power sector.
Abstract: Department of Electrical Engineering, Aligarh Muslim University, Aligarh, Uttar Pradesh, India Department of Electrical Engineering, Qatar University, Doha, Qatar Power Electronics and Renewable Energy Research Laboratory, Department of Electrical Engineering, University of Malaya, Kuala Lumpur, Malaysia School of Software and Electrical Engineering, Swinburne University of Technology, Melbourne, Victoria, Australia