Electricity Consumption Classification using Various Machine Learning Models

Authors

  • Bijay Kumar Paikaray Siksha O Anusandhan University image/svg+xml
  • Swarna Prabha Jena
  • Jayanta Mondal KIIT University image/svg+xml
  • Nguyen Van Thuan Hung Vuong University image/svg+xml
  • Nguyen Trong Tung Dong A University image/svg+xml
  • 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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Published

07-06-2024

How to Cite

1.
Paikaray BK, Prabha Jena S, Mondal J, Van Thuan N, Tung NT, Mallick C. Electricity Consumption Classification using Various Machine Learning Models. EAI Endorsed Trans Energy Web [Internet]. 2024 Jun. 7 [cited 2024 Jul. 13];11. Available from: https://publications.eai.eu/index.php/ew/article/view/6274