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A hybrid model based on symbolic regression and neural networks for electricity load forecasting
KTH, School of Electrical Engineering and Computer Science (EECS), Electric Power and Energy Systems.ORCID iD: 0000-0003-4490-9278
KTH, School of Electrical Engineering and Computer Science (EECS), Electric Power and Energy Systems.ORCID iD: 0000-0003-0685-0199
KTH, School of Electrical Engineering and Computer Science (EECS), Electric Power and Energy Systems.
KTH, School of Electrical Engineering and Computer Science (EECS), Electric Power and Energy Systems.
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2018 (English)In: 2018 15TH INTERNATIONAL CONFERENCE ON THE EUROPEAN ENERGY MARKET (EEM), IEEE , 2018Conference paper, Published paper (Refereed)
Abstract [en]

This paper proposes a hybrid model for electricity load forecasting. Symbolic regression is initially used to automatically create a regression model of the load. Then the explanatory variables and their transformations that have been selected in the model are used as input in an artificial neural network that is trained to predict the electricity load at the output. Therefore symbolic regression operates as a feature selection creation method and forecasting is done by the artificial neural network. The proposed hybrid model has been successfully used in an electricity load forecasting competition.

Place, publisher, year, edition, pages
IEEE , 2018.
Series
International Conference on the European Energy Market, ISSN 2165-4077
Keywords [en]
load forecasting, symbolic regression, neural networks, forecasting competition
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-260233DOI: 10.1109/EEM.2018.8469901ISI: 000482771100114Scopus ID: 2-s2.0-85055476841ISBN: 978-1-5386-1488-4 (print)OAI: oai:DiVA.org:kth-260233DiVA, id: diva2:1355343
Conference
15th International Conference on the European Energy Market (EEM), JUN 27-29, 2018, Lodz, POLAND
Note

QC 20190927

Available from: 2019-09-27 Created: 2019-09-27 Last updated: 2019-09-27Bibliographically approved

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Dimoulkas, IliasHerre, LarsKhastieva, DinaNycander, ElisAmelin, Mikael

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CiteExportLink to record
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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
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