Research Article
Electricity Consumption Classification using Various Machine Learning Models
@ARTICLE{10.4108/ew.6274, author={Bijay Kumar Paikaray and Swarna Prabha Jena and Jayanta Mondal and Nguyen Van Thuan and Nguyen Trong Tung and Chandrakant Mallick}, title={Electricity Consumption Classification using Various Machine Learning Models}, journal={EAI Endorsed Transactions on Energy Web}, volume={11}, number={1}, publisher={EAI}, journal_a={EW}, year={2024}, month={12}, keywords={Electricity Prediction, Machine Learning, SkLearn}, doi={10.4108/ew.6274} }- Bijay Kumar Paikaray
Swarna Prabha Jena
Jayanta Mondal
Nguyen Van Thuan
Nguyen Trong Tung
Chandrakant Mallick
Year: 2024
Electricity Consumption Classification using Various Machine Learning Models
EW
EAI
DOI: 10.4108/ew.6274
Abstract
INTRODUCTION: As population has increased over successive generations, human dependency on electricity has increased to the point where it has become a norm and indispensable, and the idea of living without it has become unthinkable. OBJECTIVES: Machine learning is emerging as a fundamental method for performing tasks autonomously without human intervention. Forecasting electricity consumption is challenging due to the many factors that influence it; embracing modern technology with its heavy focus on machine learning and artificial intelligence is a potential solution. METHODS: This study employs various machine learning algorithms to forecast power usage and determine which method performs best in predicting the dataset based on different variables. RESULTS: Eight models were tested, including Linear Regression, DT Classifier, RF Classifier, KNN, DT Regression, SVM, Logistic Regression, and GNB Classifier. The Decision Tree model had the greatest accuracy of 98.3%. CONCLUSION: The Decision Tree model’s accuracy can facilitate efficient use of electricity, leading to both conservation of electricity and cost savings, and be a guiding light in future planning.
Copyright © 2024 Paikaray, B.K. et al., licensed to EAI. This open-access article is distributed under the terms of the CC BY-NCSA 4.0, which permits copying, redistributing, remixing, transforming, and building upon the material in any medium so long as the original work is properly cited.
