Electricity Consumption Classification using Various Machine Learning Models

被引：0

作者：

Paikaray B.K. ^{[1
]}

Jena S.P. ^{[2
]}

Mondal J. ^{[3
]}

Van Thuan N. ^{[4
]}

Tung N.T. ^{[5
]}

Mallick C. ^{[6
]}

机构：

[1] Centre for Data Science, Department of Computer Science & Engineering, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar

[2] Department of Electronics and Communication Engineering, Centurion of University of Technology and Management, Odisha

[3] School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar

[4] Faculty of Engineering Technology, Hung Vuong University

[5] Quality Assurance, Dong A University

[6] Department of Computer Science & Information Technology, GITA Autonomous College, Odisha, Bhubaneswar

来源：

EAI Endorsed Transactions on Energy Web | 2024年 / 11卷

关键词：

Electricity Prediction; Machine Learning; SkLearn;

D O I：

10.4108/EW.6274

中图分类号：

学科分类号：

摘要：

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-NC-SA 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.

引用

页码：1 / 6

页数：5

共 22 条

[1] Li Kangji, Et al., Building’s electricity consumption prediction using optimized artificial neural networks and principal component analysis, Energy and Buildings, 108, pp. 106-113, (2015)
[2] Hu Yi-Chung, Electricity consumption prediction using a neural-network-based grey forecasting approach, Journal of the Operational Research Society, 68, pp. 1259-1264, (2017)
[3] Beccali M., Et al., Short-term prediction of household electricity consumption: Assessing weather sensitivity in a Mediterranean area, Renewable and Sustainable Energy Reviews, 12, 8, pp. 2040-2065, (2008)
[4] Kavaklioglu Kadir, Modeling and prediction of Turkey’s electricity consumption using Support Vector Regression, Applied Energy, 88, 1, pp. 368-375, (2011)
[5] Ding Song, Hipel Keith W., Dang Yao-guo, Forecasting China’s electricity consumption using a new grey prediction model, Energy, 149, pp. 314-328, (2018)
[6] Shine P., Et al., Annual electricity consumption prediction and future expansion analysis on dairy farms using a support vector machine, Applied energy, 250, pp. 1110-1119, (2019)
[7] Chen Kunlong, Et al., A novel data-driven approach for residential electricity consumption prediction based on ensemble learning, Energy, 150, pp. 49-60, (2018)
[8] Lin Zhifeng, Cheng Lianglun, Huang Guoheng, Electricity consumption prediction based on LSTM with attention mechanism, IEEJ Transactions on Electrical and Electronic Engineering, 15, 4, pp. 556-562, (2020)
[9] Li Kangji, Et al., Short-term electricity consumption prediction for buildings using data-driven swarm intelligence-based ensemble model, Energy and Buildings, 231, (2021)
[10] Xu Ning, Dang Yaoguo, Gong Yande, Novel grey prediction model with nonlinear optimized time response method for forecasting of electricity consumption in China, Energy, 118, pp. 473-480, (2017)

← 1 2 3 →