Machine Learning-Based Model for Prediction of Power Consumption in Smart Grid
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Shamik Tiwari School of Computer Science, University of Petroleum and Energy Studies, India This email address is being protected from spambots. You need JavaScript enabled to view it. |
Anurag Jain* School of Computer Science, University of Petroleum and Energy Studies, India This email address is being protected from spambots. You need JavaScript enabled to view it. *corresponding author |
Kusum Yadav Computer Science and Information Systems, University of Hail, Hail This email address is being protected from spambots. You need JavaScript enabled to view it. |
Rabie Ramadan Computer Science and Information Systems, University of Hail, Hail This email address is being protected from spambots. You need JavaScript enabled to view it. |
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.
Keywords: Smart grid, grid stability, machine learning, load balancing, prediction model.
Received September 18, 2020; accepted August 31, 2021
https://doi.org/10.34028/iajit/19/3/5