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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


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Machine learning algorithm, electricity, consumption models, variables