Model for the Forecast of the purchase of energy in a Utility through artificial neural networks with penetration of renewable energies

Marco A. Toledo, Carlos Alvarez-Bel, Flavio A. Quizhpi, Miguel A. Figueroa, Jordy A. Pintado

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

Abstract

Research develops a planning model to carry out the prognosis of energy purchase that the distribution and marketing company is done through the use of energy demand information and with the penetration of renewable generation in the short and medium-term using a computational model of artificial neuronal networks in the MATLAB computational tool, the results obtained show the performance of this model with errors less than 1% both in training and prediction. For the respective testing of this algorithm, the historical data of 5 years of the 'Electric Regional Enterprise Sur Centro C. A.' was taken of the city of Cuenca in Ecuador.

Original languageEnglish
Title of host publication2021 23rd IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665434270
DOIs
StatePublished - 2021
Event23rd IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2021 - Virtual, Ixtapa, Mexico
Duration: 10 Nov 202112 Nov 2021

Publication series

Name2021 23rd IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2021

Conference

Conference23rd IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2021
Country/TerritoryMexico
CityVirtual, Ixtapa
Period10/11/2112/11/21

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

Keywords

  • Artificial Neural Networks
  • Demand
  • Forecasting
  • Planning
  • Renewable Energy
  • Short -Medium Term

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