Short-term electricity load forecasting with machine learning

Bibliographic Details
Main Author: Aguilar Madrid, Ernesto
Publication Date: 2021
Other Authors: António, Nuno
Format: Article
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10362/112349
Summary: Aguilar Madrid, E., & Antonio, N. (2021). Short-term electricity load forecasting with machine learning. Information (Switzerland), 12(2), 1-21. [50]. https://doi.org/10.3390/info12020050
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network_name_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
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spelling Short-term electricity load forecasting with machine learningElectricityElectricity marketMachine learningShort-term load forecastingWeekly forecastInformation SystemsSDG 9 - Industry, Innovation, and InfrastructureAguilar Madrid, E., & Antonio, N. (2021). Short-term electricity load forecasting with machine learning. Information (Switzerland), 12(2), 1-21. [50]. https://doi.org/10.3390/info12020050An accurate short-term load forecasting (STLF) is one of the most critical inputs for power plant units’ planning commitment. STLF reduces the overall planning uncertainty added by the intermittent production of renewable sources; thus, it helps to minimize the hydrothermal electricity production costs in a power grid. Although there is some research in the field and even several research applications, there is a continual need to improve forecasts. This research proposes a set of machine learning (ML) models to improve the accuracy of 168 h forecasts. The developed models employ features from multiple sources, such as historical load, weather, and holidays. Of the five ML models developed and tested in various load profile contexts, the Extreme Gradient Boosting Regressor (XGBoost) algorithm showed the best results, surpassing previous historical weekly predictions based on neural networks. Additionally, because XGBoost models are based on an ensemble of decision trees, it facilitated the model’s interpretation, which provided a relevant additional result, the features’ importance in the forecasting.NOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNAguilar Madrid, ErnestoAntónio, Nuno2021-02-24T02:07:30Z2021-02-202021-02-20T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article21application/pdfhttp://hdl.handle.net/10362/112349eng2078-2489PURE: 28265368https://doi.org/10.3390/info12020050info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-09-23T01:38:08Zoai:run.unl.pt:10362/112349Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T17:21:48.601267Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Short-term electricity load forecasting with machine learning
title Short-term electricity load forecasting with machine learning
spellingShingle Short-term electricity load forecasting with machine learning
Aguilar Madrid, Ernesto
Electricity
Electricity market
Machine learning
Short-term load forecasting
Weekly forecast
Information Systems
SDG 9 - Industry, Innovation, and Infrastructure
title_short Short-term electricity load forecasting with machine learning
title_full Short-term electricity load forecasting with machine learning
title_fullStr Short-term electricity load forecasting with machine learning
title_full_unstemmed Short-term electricity load forecasting with machine learning
title_sort Short-term electricity load forecasting with machine learning
author Aguilar Madrid, Ernesto
author_facet Aguilar Madrid, Ernesto
António, Nuno
author_role author
author2 António, Nuno
author2_role author
dc.contributor.none.fl_str_mv NOVA Information Management School (NOVA IMS)
Information Management Research Center (MagIC) - NOVA Information Management School
RUN
dc.contributor.author.fl_str_mv Aguilar Madrid, Ernesto
António, Nuno
dc.subject.por.fl_str_mv Electricity
Electricity market
Machine learning
Short-term load forecasting
Weekly forecast
Information Systems
SDG 9 - Industry, Innovation, and Infrastructure
topic Electricity
Electricity market
Machine learning
Short-term load forecasting
Weekly forecast
Information Systems
SDG 9 - Industry, Innovation, and Infrastructure
description Aguilar Madrid, E., & Antonio, N. (2021). Short-term electricity load forecasting with machine learning. Information (Switzerland), 12(2), 1-21. [50]. https://doi.org/10.3390/info12020050
publishDate 2021
dc.date.none.fl_str_mv 2021-02-24T02:07:30Z
2021-02-20
2021-02-20T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/112349
url http://hdl.handle.net/10362/112349
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2078-2489
PURE: 28265368
https://doi.org/10.3390/info12020050
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 21
application/pdf
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
_version_ 1833596642783133696

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