id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
ejpam-5953	Danach, Kassem ; Aly, Wael Hosny Fouad; Kanj, Hassan	Machine Learning for Smart Grid Stability: Enhancing Reliability in Renewable Energy Integration	2025	20	.pdf	application/pdf	5973	381	45	To address the challenges in smart grid stability, this study seeks to answer the following key research questions: (i) How effectively can machine learning models predict grid stability in scenarios with renewable energy integration? (ii) This study explores the application of machine learning techniques to pre- dict and enhance smart grid stability, focusing on scenarios involving renewable energy integration.	cache/ejpam-5953.pdf	txt/ejpam-5953.txt