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

Johnny Xavier Serrano Guerrero, Ricardo Manuel Prieto Galarza

Research output: Contribution to conferencePaper

4 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 (US)
DOIs
StatePublished - 18 Jan 2018
Event2017 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2017 - Ixtapa, Mexico
Duration: 8 Nov 201710 Nov 2017

Conference

Conference2017 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2017
Abbreviated titleROPEC 2017
CountryMexico
CityIxtapa
Period8/11/1710/11/17

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    Serrano Guerrero, J. X., & Prieto Galarza, R. M. (2018). Election of variables and short-term forecasting of electricity demand based on backpropagation artificial neural networks. Paper presented at 2017 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2017, Ixtapa, Mexico. https://doi.org/10.1109/ROPEC.2017.8261630