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Short-term load forecasting using time series clustering

2022-08-10Journal article Restricted access
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dc.contributor.authorMartins, Ana Alexandra
dc.contributor.authorLagarto, João
dc.contributor.authorCanacsinh, Hiren
dc.contributor.authorReis, Francisco
dc.contributor.authorCardoso, Maria Margarida
dc.date.accessioned2023-05-03T14:43:24Z
dc.date.available2023-05-03T14:43:24Z
dc.date.issued2022-08-10
dc.description.abstractShort-term load forecasting plays a major role in energy planning. Its accuracy has a direct impact on the way power systems are operated and managed. We propose a new Clustering-based Similar Pattern Forecasting algorithm (CSPF) for short-term load forecasting. It resorts to a K-Medoids clustering algorithm to identify load patterns and to the COMB distance to capture differences between time series. Clusters' labels are then used to identify similar sequences of days. Temperature information is also considered in the day-ahead load forecasting, resorting to the K-Nearest Neighbor approach. CSPF algorithm is intended to provide the aggregate forecast of Portugal's national load, for the next day, with a 15-min discretization, based on data from the Portuguese Transport Network Operator (TSO). CSPF forecasting performance, as evaluated by RMSE, MAE and MAPE metrics, outperforms three alternative/baseline methods, suggesting that the proposed approach is promising in similar applications.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationMARTINS, Ana; [et al] – Short-term load forecasting using time series clustering. Optimization and Engineering. ISSN 1389-4420. Vol. 23, N.º 4 (2022), pp. 2293-2314.pt_PT
dc.identifier.doi10.1007/s11081-022-09760-1pt_PT
dc.identifier.eissn1573-2924
dc.identifier.issn1389-4420
dc.identifier.urihttp://hdl.handle.net/10400.21/15969
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSpringerpt_PT
dc.relationIPL/2020/ELForcast_ISEL - Instituto Politécnico de Lisboapt_PT
dc.relationBusiness Research Unit - BRU-IUL
dc.relationInstituto de Engenharia de Sistemas e Computadores, Investigação e Desenvolvimento em Lisboa
dc.relation.publisherversionhttps://link.springer.com/article/10.1007/s11081-022-09760-1pt_PT
dc.subjectClustering time seriespt_PT
dc.subjectDistance measurespt_PT
dc.subjectLoad pattern sequencept_PT
dc.subjectPattern similarpt_PT
dc.subjectPattern Methodpt_PT
dc.subjectShort-term load forecastingpt_PT
dc.titleShort-term load forecasting using time series clusteringpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleBusiness Research Unit - BRU-IUL
oaire.awardTitleInstituto de Engenharia de Sistemas e Computadores, Investigação e Desenvolvimento em Lisboa
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00315%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50021%2F2020/PT
oaire.citation.endPage2314pt_PT
oaire.citation.issue4pt_PT
oaire.citation.startPage2293pt_PT
oaire.citation.titleOptimization and Engineeringpt_PT
oaire.citation.volume23pt_PT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
person.familyNameMartins
person.familyNameLagarto
person.familyNameCanacsinh
person.familyNameCardoso
person.givenNameAna Alexandra
person.givenNameJoão
person.givenNameHiren
person.givenNameMaria Margarida
person.identifier1017796
person.identifier.ciencia-id2016-88BF-5D0B
person.identifier.ciencia-id0512-2920-3C9E
person.identifier.ciencia-id1E13-A798-9EA2
person.identifier.ciencia-id3E1B-1DAD-9287
person.identifier.orcid0000-0003-3733-6619
person.identifier.orcid0000-0002-7047-6210
person.identifier.orcid0000-0002-7531-5459
person.identifier.orcid0000-0001-6239-7283
person.identifier.scopus-author-id34969159800
person.identifier.scopus-author-id24758947600
person.identifier.scopus-author-id21233265300
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.rightsclosedAccesspt_PT
rcaap.typearticlept_PT
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