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Day-ahead electricity market price forecasting using artificial neural network with spearman data correlation

2019Journal article Open access
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dc.contributor.authorNascimento, Joao
dc.contributor.authorPinto, Tiago
dc.contributor.authorVale, Zita
dc.date.accessioned2021-09-22T14:42:31Z
dc.date.available2021-09-22T14:42:31Z
dc.date.issued2019
dc.description.abstractElectricity markets are complex environments with very dynamic characteristics. The large-scale penetration of renewable energy sources has brought an increased uncertainty to generation, which is consequently, reflected in electricity market prices. In this way, novel advanced forecasting methods that are able to predict electricity market prices taking into account the new variables that influence prices variation are required. This paper proposes a new model for day-ahead electricity market prices forecasting based on the application of an artificial neural network. The main novelty of this paper relates to the pre-processing phase, in which the relevant data referring to the different variables that have a direct influence on market prices such as generation, temperature, consumption, among others, is analysed. The association between these variables is performed using spearman correlation, from which results the identification of which data has a larger influence on the market prices variation. This pre-analysis is then used to adapt the training process of the artificial neural network, leading to improved forecasting results, by using the most relevant data in an appropriate way.pt_PT
dc.description.sponsorshipThis work has been developed under the MAS-SOCIETY project - PTDC/EEI-EEE/28954/2017 and received funding from UID/EEA/00760/2019, funded by FEDER Funds through COMPETE and by National Funds through FCT.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1109/PTC.2019.8810618pt_PT
dc.identifier.isbn978-1-5386-4722-6
dc.identifier.urihttp://hdl.handle.net/10400.22/18484
dc.language.isoengpt_PT
dc.publisherIEEEpt_PT
dc.relationMulti-Agent Systems SemantiC Interoperability for simulation and dEcision supporT in complex energY systems
dc.relationResearch Group on Intelligent Engineering and Computing for Advanced Innovation and Development
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/8810618pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/pt_PT
dc.subjectArtificial neural networkspt_PT
dc.subjectDay-ahead spot marketpt_PT
dc.subjectElectricity pricept_PT
dc.subjectForecastingpt_PT
dc.subjectSpearman correlationpt_PT
dc.titleDay-ahead electricity market price forecasting using artificial neural network with spearman data correlationpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleMulti-Agent Systems SemantiC Interoperability for simulation and dEcision supporT in complex energY systems
oaire.awardTitleResearch Group on Intelligent Engineering and Computing for Advanced Innovation and Development
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/9471 - RIDTI/PTDC%2FEEI-EEE%2F28954%2F2017/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UID%2FEEA%2F00760%2F2019/PT
oaire.citation.conferencePlaceMilan, Italypt_PT
oaire.citation.endPage6pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.title2019 IEEE Milan PowerTechpt_PT
oaire.fundingStream9471 - RIDTI
oaire.fundingStream6817 - DCRRNI ID
person.familyNamePinto
person.familyNameVale
person.givenNameTiago
person.givenNameZita
person.identifierR-000-T7J
person.identifier632184
person.identifier.ciencia-id2414-9B03-C4BB
person.identifier.ciencia-id721B-B0EB-7141
person.identifier.orcid0000-0001-8248-080X
person.identifier.orcid0000-0002-4560-9544
person.identifier.ridT-2245-2018
person.identifier.ridA-5824-2012
person.identifier.scopus-author-id35219107600
person.identifier.scopus-author-id7004115775
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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