Machine Learning Models for Electricity Consumption Forecasting: A Review

Alfonso González-Briones, Guillermo Hernandez, Juan M. Corchado, Sigeru Omatu, Mohd Saberi Mohamad

Research output: Chapter in Book/Report/Conference proceedingConference contribution

48 Citations (Scopus)

Abstract

The prediction of energy consumption is a task that allows energy supply companies to adapt to certain behaviors. Among these activities that companies can perform is to know the behavior of their customers to adapt their rates to consumption or know the intervals in which it will produce a greater demand for energy and have planned the adaptation of supply chains. In this sense, it is necessary to carry out an evaluation of methods that allow forecasting future energy consumption based on the consumption history and other variables of the users themselves. In this article, a review of the main machine learning models that allow predicting energy consumption using a one-year data set of a shoe store was made. The review made allowed to observe that for the data set using the Linear Regression and Support Vector Regression has obtained a success of 85.7% being the best results provided.

Original languageEnglish
Title of host publication2nd International Conference on Computer Applications and Information Security, ICCAIS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728101088
DOIs
Publication statusPublished - May 2019
Externally publishedYes
Event2nd International Conference on Computer Applications and Information Security, ICCAIS 2019 - Riyadh, Saudi Arabia
Duration: May 1 2019May 3 2019

Publication series

Name2nd International Conference on Computer Applications and Information Security, ICCAIS 2019

Conference

Conference2nd International Conference on Computer Applications and Information Security, ICCAIS 2019
Country/TerritorySaudi Arabia
CityRiyadh
Period5/1/195/3/19

Keywords

  • Decision Tree
  • Energy Forecasting
  • K-nearest Neighbours
  • Linear Regression
  • Machine Learning
  • Random Forest
  • Support Vector Regression

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems and Management
  • Health Informatics
  • Information Systems
  • Safety, Risk, Reliability and Quality
  • Artificial Intelligence

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