Electricity Consumption Classification using Various Machine Learning Models

Bijay Kumar Paikaray; Swarna Prabha Jena; Jayanta Mondal; Nguyen Van Thuan; Nguyen Trong Tung; Chandrakant Mallick

doi:10.4108/ew.6274

Authors

Bijay Kumar Paikaray Siksha O Anusandhan University
Swarna Prabha Jena
Jayanta Mondal KIIT University
Nguyen Van Thuan Hung Vuong University
Nguyen Trong Tung Dong A University
Chandrakant Mallick Gandhi Institute of Technological Advancement

DOI:

https://doi.org/10.4108/ew.6274

Keywords:

Electricity Prediction, Machine Learning, SkLearn

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.

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