Call for Paper

CAE solicits original research papers for the May 2023 Edition. Last date of manuscript submission is April 30, 2023.

Read More

A Survey of Machine Learning’s Electricity Consumption Models

Umar Farouk Ibn Abdulrahman, Michael Asante, James Ben Hayfron-Acquah. Published in Artificial Intelligence.

Communications on Applied Electronics
Year of Publication: 2018
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: Umar Farouk Ibn Abdulrahman, Michael Asante, James Ben Hayfron-Acquah

Umar Farouk Ibn Abdulrahman, Michael Asante and James Ben Hayfron-Acquah. A Survey of Machine Learning’s Electricity Consumption Models. Communications on Applied Electronics 7(21):6-10, October 2018. BibTeX

	author = {Umar Farouk Ibn Abdulrahman and Michael Asante and James Ben Hayfron-Acquah},
	title = {A Survey of Machine Learning’s Electricity Consumption Models},
	journal = {Communications on Applied Electronics},
	issue_date = {October 2018},
	volume = {7},
	number = {21},
	month = {Oct},
	year = {2018},
	issn = {2394-4714},
	pages = {6-10},
	numpages = {5},
	url = {},
	doi = {10.5120/cae2018652789},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Electricity is a very important commodity used for both domestic and industrial purposes. It is generated from many sources which include the thermal, coal, nuclear and hydro. Its demand is increasing on regular basis as result of the ever increasing world population coupled with other socio-economic factors. This therefore requires effective predictions of the future needed electricity to sustain it demand. However, predicting the exact amount of electricity for all times is a challenge. Over predictions can lead to wasteful investment whiles under predictions can lead to inadequate electricity supply with eventual blackouts, social unrest and low economic growth. The aim of this paper is to present the various electricity consumption predictions models indicating the machine learning algorithm and the variables used in the modeling


  1. Kialashaki, A., & Reisel, J. R. (2013). Modeling of the energy demand of the residential sector in the United States using regression models and artificial neural networks. Applied Energy, 108, 271-280
  2. Wang, L. (2016). Machine learning in big data. International Journal of Advances in Applied Sciences, 4(4), 117-123.
  3. ML, 2018, Accessed on 18th February, 2018
  4. Tu, C., He, X., Shuai, Z., & Jiang, F. (2017). Big data issues in smart grid–A review. Renewable and Sustainable Energy Reviews, 79, 1099-1107.
  5. Kaboli, S. H. A., Fallahpour, A., Selvaraj, J., & Rahim, N. A. (2017). Long-term electrical energy consumption formulating and forecasting via optimized gene expression programming. Energy, 126, 144-164.
  6. Kavaklioglu, K. (2011). Modeling and prediction of Turkey’s electricity consumption using Support Vector Regression. Applied Energy, 88(1), 368-375.
  7. Marvuglia, A., & Messineo, A. (2012). Using recurrent artificial neural networks to forecast household electricity consumption. Energy Procedia, 14, 45-55.
  8. Ahmad, A. S., Hassan, M. Y., Abdullah, M. P., Rahman, H. A., Hussin, F., Abdullah, H., & Saidur, R. (2014). A review on applications of ANN and SVM for building electrical energy consumption forecasting. Renewable and Sustainable Energy Reviews, 33, 102-109.
  9. Grolinger, K., L’Heureux, A., Capretz, M. A., & Seewald, L. (2016). Energy forecasting for event venues: big data and prediction accuracy. Energy and Buildings, 112, 222-233.
  10. Bianco, V., Manca, O., & Nardini, S. (2009). Electricity consumption forecasting in Italy using linear regression models. Energy, 34(9), 1413-1421.
  11. Ekonomou, L. (2010). Greek long-term energy consumption prediction using artificial neural networks. Energy, 35(2), 512-517.
  12. Amjadi, M.H., Nezamabadi-pour, H and Farsangi, M.M, Estimation of electricity demand of Iran using two heuristic algorithms,Energy Conversion and Management 51 (2010) 493–497
  13. Edwards, R. E., New, J., & Parker, L. E. (2012). Predicting future hourly residential electrical consumption: A machine learning case study. Energy and Buildings, 49, 591-603
  14. Kıran, M. S., Özceylan, E., Gündüz, M., & Paksoy, T. (2012). A novel hybrid approach based on particle swarm optimization and ant colony algorithm to forecast energy demand of Turkey. Energy conversion and management, 53(1), 75-83.
  15. Couceiro, M., Ferrando, R., Manzano, D., & Lafuente, L. (2012, May). Stream analytics for utilities. Predicting power supply and demand in a smart grid. In Cognitive Information Processing (CIP), 2012 3rd International Workshop on (pp. 1-6). IEEE.
  16. Ardakani, F. J., & Ardehali, M. M. (2014). Long-term electrical energy consumption forecasting for developing and developed economies based on different optimized models and historical data types. Energy, 65, 452-461.
  17. Kargar, M. J., & Charsoghi, D. K. (2014). Predicting annual electricity consumption in Iran using artificial neural networks (NARX). Indian J. Sci. Res, 5(1), 231-242.
  18. Jovanović, R. Ž., Sretenović, A. A., & Živković, B. D. (2015). Ensemble of various neural networks for prediction of heating energy consumption. Energy and Buildings, 94, 189-199.
  19. Günay, M. E. (2016). Forecasting annual gross electricity demand by artificial neural networks using predicted values of socio-economic indicators and climatic conditions: Case of Turkey. Energy Policy, 90, 92-101.
  20. Birim, Ş., & Tümtürk, A. (2016). Modeling and Forecasting Turkey’s Electricity Consumption by using Artificial Neural Network. American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS), 25(1), 192-208.
  21. Zufferey, T., Ulbig, A., Koch, S., & Hug, G. (2016, September). Forecasting of smart meter time series based on neural networks. In International Workshop on Data Analytics for Renewable Energy Integration (pp. 10-21). Springer, Cham.,
  22. Rahman, A., Srikumar, V., & Smith, A. D. (2018). Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks. Applied Energy, 212, 372-385.
  23. Tso, G. K., & Yau, K. K. (2007). Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks. Energy, 32(9), 1761-1768.


Machine learning algorithm, electricity, consumption models, variables