Electricity Consumption Classification using Various Machine Learning Models


  • 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




Electricity Prediction, Machine Learning, SkLearn


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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Li, Kangji, et al. "Building's electricity consumption prediction using optimized artificial neural networks and principal component analysis." Energy and Buildings 108 (2015): 106-113. DOI: https://doi.org/10.1016/j.enbuild.2015.09.002

Hu, Yi-Chung. "Electricity consumption prediction using a neural-network-based grey forecasting approach." Journal of the Operational Research Society 68 (2017): 1259-1264. DOI: https://doi.org/10.1057/s41274-016-0150-y

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 (2008): 2040-2065. DOI: https://doi.org/10.1016/j.rser.2007.04.010

Kavaklioglu, Kadir. "Modeling and prediction of Turkey's electricity consumption using Support Vector Regression." Applied Energy 88.1 (2011): 368-375.

Ding, Song, Keith W. Hipel, and Yao-guo Dang. "Forecasting China's electricity consumption using a new grey prediction model." Energy 149 (2018): 314-328. DOI: https://doi.org/10.1016/j.energy.2018.01.169

Shine, P., et al. "Annual electricity consumption prediction and future expansion analysis on dairy farms using a support vector machine." Applied energy 250 (2019): 1110-1119. DOI: https://doi.org/10.1016/j.apenergy.2019.05.103

Chen, Kunlong, et al. "A novel data-driven approach for residential electricity consumption prediction based on ensemble learning." Energy 150 (2018): 49-60. DOI: https://doi.org/10.1016/j.energy.2018.02.028

Lin, Zhifeng, Lianglun Cheng, and Guoheng Huang. "Electricity consumption prediction based on LSTM with attention mechanism." IEEJ Transactions on Electrical and Electronic Engineering 15.4 (2020): 556-562. DOI: https://doi.org/10.1002/tee.23088

Li, Kangji, et al. "Short-term electricity consumption prediction for buildings using data-driven swarm intelligence-based ensemble model." Energy and Buildings 231 (2021): 110558. DOI: https://doi.org/10.1016/j.enbuild.2020.110558

Xu, Ning, Yaoguo Dang, and Yande Gong. "Novel grey prediction model with nonlinear optimized time response method for forecasting of electricity consumption in China." Energy 118 (2017): 473-480. DOI: https://doi.org/10.1016/j.energy.2016.10.003

W Wang, Y Shi, G Lyu, & W Deng (2017). Electricity consumption prediction using XGBoost based on discrete wavelet transform. DEStech Transactions on Computer Science and Engineering. DOI: https://doi.org/10.12783/dtcse/aiea2017/15003

Kadir Kavaklioglu (2011). Modeling and prediction of Turkey's electricity consumption using Support Vector Regression. Applied Energy, 88(1), 368-375. DOI: https://doi.org/10.1016/j.apenergy.2010.07.021

Lambros Ekonomou (2010). Greek long-term energy consumption prediction using artificial neural networks. Energy, 35(2), 512-517. DOI: https://doi.org/10.1016/j.energy.2009.10.018

Ghosh, H., Rahat, I. S., Mohanty, S. N., & Ramesh, J. V. N. (2023). Microbial Image Deciphering: Navigating Challenges with Machine and Deep Learning. DOI: https://doi.org/10.21203/rs.3.rs-3633958/v1

Platon, R., Dehkordi, V. R., & Martel, J. (2015). Hourly prediction of a building's electricity consumption using case-based reasoning, artificial neural networks and principal component analysis. Energy and Buildings, 92, 10-18. DOI: https://doi.org/10.1016/j.enbuild.2015.01.047

Magoulès, F., Piliougine, M., & Elizondo, D. (2016, August). Support vector regression for electricity consumption prediction in a building in japan. In 2016 IEEE Intl Conference on Computational Science and Engineering (CSE) and IEEE Intl Conference on Embedded and Ubiquitous Computing (EUC) and 15th Intl Symposium on Distributed Computing and Applications for Business Engineering (DCABES) (pp. 189-196). IEEE. DOI: https://doi.org/10.1109/CSE-EUC-DCABES.2016.184

Sun, S., & Chen, H. (2021, August). Data-driven sensitivity analysis and electricity consumption prediction for water source heat pump system using limited information. In Building Simulation (Vol. 14, pp. 1005-1016). Tsinghua University Press. DOI: https://doi.org/10.1007/s12273-020-0721-3

Jena, S. P., Paikaray, B. K., Pramanik, J., Thapa, R., & Samal, A. K. (2023). Classifications on wine informatics using PCA, LDA, and supervised machine learning techniques. International Journal of Work Innovation, 4(1), 58-73. DOI: https://doi.org/10.1504/IJWI.2023.130444

Hora, S. K., Poongodan, R., De Prado, R. P., Wozniak, M., & Divakarachari, P. B. (2021). Long short-term memory network-based metaheuristic for effective electric energy consumption prediction. Applied Sciences, 11(23), 11263. DOI: https://doi.org/10.3390/app112311263

Chinnaraji, R., & Ragupathy, P. (2022). Accurate electricity consumption prediction using enhanced long short‐term memory. IET Communications, 16(8), 830-844. DOI: https://doi.org/10.1049/cmu2.12384

Jena, S. P., Yadav, A. K., Gupta, D., & Paikaray, B. K. (2023, September). Prediction of Stock Price Using Machine Learning Techniques. In 2023 IEEE 2nd International Conference on Industrial Electronics: Developments & Applications (ICIDeA) (pp. 169-174). IEEE. DOI: https://doi.org/10.1109/ICIDeA59866.2023.10295232

Zielińska-Sitkiewicz, M., Chrzanowska, M., Furmańczyk, K., & Paczutkowski, K. (2021). Analysis of electricity consumption in Poland using prediction models and neural networks. Energies, 14(20), 6619. DOI: https://doi.org/10.3390/en14206619




How to Cite

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