Abstract An electric grid consists of transformers, generation centers, communication links, control stations, and distributors. Collectively these components help in moving power from one electricity station to commercial and domestic consumers. Traditional grid stations can’t predict the dynamic need of consumers’ electricity. Furthermore, these traditional grids are not sufficiently strong and adaptable. This is the driving force for the transition towards a smart grid. A modern smart grid is a self-healing, long-lasting electrical system that can adapt to changing client needs. Machine learning has aided in grid stability calculation in the face of dynamically shifting consumer demands. By avoiding a breakdown, the smart grid has been transformed into a reliable smart grid. The authors of this study used a variety of machine learning-based algorithms to estimate grid stability to avoid a breakdown situation. An open-access dataset lying on Kaggle repository has been used for experimental work. Experiments are conducted in a simulation environment generated through Python. Using the Bagging classifier algorithm, the suggested model has attained an accuracy level of 97.9% while predicting the load. A precise prediction of power demand will aid in the avoidance of grid failure, hence improving grid stability and robustness.

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[28] Yu W., An D., Griffith D., Yang Q., and Xu G., “Towards Statistical Modeling and Machine Learning Based Energy Usage Forecasting in Smart Grid,” ACM SIGAPP Applied Computing Review, vol. 15, no. 1, pp. 6-16, 2015. Shamik Tiwari is a Sr Associate Professor in the Systemics cluster at the UPES in Dehradun's. He has around 18 years of academic experience. DIP, ML, DL, computer vision, and health informatics are some of his research interests. He has authored many international and national publications. Anurag Jain is an Associate Professor in the Systemics cluster at the UPES in Dehradun's. For the past eighteen years, he is serving in the field of academics. He has published around 50 research articles in prestigious conferences and journals. His research interests include healthcare, machine learning, scheduling, and load balancing. Kusum Yadav holds an MCA, M- Tech, and a Ph.D. in Network Security. She has more than 14 years of experience as a teacher. She works as an Associate Professor at the University of Hail in the Saudi Arabian Kingdom of Saudi Arabia. She is involved in the Internet of Things, Blockchain, Machine Learning, and Artificial Intelligence fields. She has authored two books on computer programming and internet applications. And has also published over 35 research papers in a variety of prestigious international journals. Rabie Ramadan is an associate professor serving in University of Hail, Hail, in the fields of Internet of Things (IoT), Mobile Computing, and Computational Intelligence. He has an academic and indistrial experience of around 27 years. His more details are available at https://rabieramadan.org/.