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Reseach Article

A Survey of Machine Learning’s Electricity Consumption Models

by Umar Farouk Ibn Abdulrahman, Michael Asante, James Ben Hayfron-Acquah
Communications on Applied Electronics
Foundation of Computer Science (FCS), NY, USA
Volume 7 - Number 21
Year of Publication: 2018
Authors: Umar Farouk Ibn Abdulrahman, Michael Asante, James Ben Hayfron-Acquah

Umar Farouk Ibn Abdulrahman, Michael Asante, James Ben Hayfron-Acquah . A Survey of Machine Learning’s Electricity Consumption Models. Communications on Applied Electronics. 7, 21 ( Oct 2018), 6-10. DOI=10.5120/cae2018652789

@article{ 10.5120/cae2018652789,
author = { Umar Farouk Ibn Abdulrahman, Michael Asante, James Ben Hayfron-Acquah },
title = { A Survey of Machine Learning’s Electricity Consumption Models },
journal = { Communications on Applied Electronics },
issue_date = { Oct 2018 },
volume = { 7 },
number = { 21 },
month = { Oct },
year = { 2018 },
issn = { 2394-4714 },
pages = { 6-10 },
numpages = {9},
url = { },
doi = { 10.5120/cae2018652789 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2023-09-04T20:03:45.264209+05:30
%A Umar Farouk Ibn Abdulrahman
%A Michael Asante
%A James Ben Hayfron-Acquah
%T A Survey of Machine Learning’s Electricity Consumption Models
%J Communications on Applied Electronics
%@ 2394-4714
%V 7
%N 21
%P 6-10
%D 2018
%I Foundation of Computer Science (FCS), NY, 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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Index Terms

Computer Science
Information Sciences


Machine learning algorithm electricity consumption models variables