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

doi:10.1109/ROPEC.2017.8261630

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 proceeding › Conference contribution › peer-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 language	English
Title of host publication	2017 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1-5
Number of pages	5
ISBN (Electronic)	9781538608197
DOIs	https://doi.org/10.1109/ROPEC.2017.8261630
State	Published - 16 Jan 2018
Event	2017 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2017 - Ixtapa, Guerrero, Mexico Duration: 8 Nov 2017 → 10 Nov 2017

Publication series

Name	2017 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2017
Volume	2018-January

Conference

Conference	2017 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2017
Abbreviated title	ROPEC 2017
Country/Territory	Mexico
City	Ixtapa, Guerrero
Period	8/11/17 → 10/11/17

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

Keywords

artificial neural networks
electricity demand
forecasting
prediction

Access to Document

10.1109/ROPEC.2017.8261630

Cite this

Serrano-Guerrero, X., Prieto-Galarza, R., Huilcatanda, E., Cabrera-Zeas, J., & Escriva-Escriva, G. (2018). Election of variables and short-term forecasting of electricity demand based on backpropagation artificial neural networks. In 2017 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2017 (pp. 1-5). (2017 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2017; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ROPEC.2017.8261630

Serrano-Guerrero, Xavier ; Prieto-Galarza, Ricardo ; Huilcatanda, Esteban et al. / Election of variables and short-term forecasting of electricity demand based on backpropagation artificial neural networks. 2017 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2017. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 1-5 (2017 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2017).

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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).",

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Serrano-Guerrero, X, Prieto-Galarza, R, Huilcatanda, E, Cabrera-Zeas, J & Escriva-Escriva, G 2018, Election of variables and short-term forecasting of electricity demand based on backpropagation artificial neural networks. in 2017 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2017. 2017 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2017, vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 1-5, 2017 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2017, Ixtapa, Guerrero, Mexico, 8/11/17. https://doi.org/10.1109/ROPEC.2017.8261630

Election of variables and short-term forecasting of electricity demand based on backpropagation artificial neural networks. / Serrano-Guerrero, Xavier; Prieto-Galarza, Ricardo; Huilcatanda, Esteban et al.
2017 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2017. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1-5 (2017 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2017; Vol. 2018-January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - 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).

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Serrano-Guerrero X, Prieto-Galarza R, Huilcatanda E, Cabrera-Zeas J, Escriva-Escriva G. Election of variables and short-term forecasting of electricity demand based on backpropagation artificial neural networks. In 2017 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2017. Institute of Electrical and Electronics Engineers Inc. 2018. p. 1-5. (2017 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2017). doi: 10.1109/ROPEC.2017.8261630

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

Abstract

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Bibliographical note

Keywords

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