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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/12958

Title: A Survey of Machine Learning’s Electricity Consumption Models
Authors: Umar Farouk, Ibn Abdulrahman
Asante, Michael
Hayfron-Acquah, James Ben
Keywords: Machine learning algorithm
electricity
consumption models
variables
Issue Date: Oct-2018
Publisher: Communications on Applied Electronics
Citation: Communications on Applied Electronics,Volume 7 – No. 21
Abstract: 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 socioeconomic 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
Description: This article is published in Communications on Applied Electronics and also available at DOI: 10.5120/cae2018652789
URI: 10.5120/cae2018652789
http://hdl.handle.net/123456789/12958
Appears in Collections:College of Science

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