Election of variables and short-term forecasting of electricity demand based on backpropagation artificial neural networks

Xavier Serrano-Guerrero, Ricardo Prieto-Galarza, Esteban Huilcatanda, Juan Cabrera-Zeas, Guillermo Escriva-Escriva

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations

Abstract

Forecasting of electricity demand is a fundamental requirement for the energy sector since from its results important decisions are taken. The areas involved are maintenance of electrical networks, demand growth, increased installed capacity, among others, whose lack of precision can take high economic costs. In this work, we propose a method based on backpropagation neural networks and election of key variables as inputs. The number of neurons in the hidden layer was optimized. To avoid the overtraining the best time range of data was defined. The results show that the method works particularly well for short-term forecasting (24 or 48 hours).

Original languageEnglish
Title of host publication2017 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-5
Number of pages5
ISBN (Electronic)9781538608197
DOIs
StatePublished - 16 Jan 2018
Event2017 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2017 - Ixtapa, Guerrero, Mexico
Duration: 8 Nov 201710 Nov 2017

Publication series

Name2017 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2017
Volume2018-January

Conference

Conference2017 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2017
Country/TerritoryMexico
CityIxtapa, Guerrero
Period8/11/1710/11/17

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

Keywords

  • artificial neural networks
  • electricity demand
  • forecasting
  • prediction

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