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Please use this identifier to cite or link to this item: https://rima.ufrrj.br/jspui/handle/20.500.14407/14353
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dc.contributor.authorFilgueiras, Douglas Bortolassi
dc.date.accessioned2023-12-22T03:00:06Z-
dc.date.available2023-12-22T03:00:06Z-
dc.date.issued2022-05-30
dc.identifier.citationFILGUEIRAS, Douglas Bortolassi. Um estudo sobre a previsão do consumo de energia elétrica considerando as medidas de eficiência energética: aplicado à Universidade Federal Rural do Rio de Janeiro. 2022. 61 f. Dissertação (Mestrado em Modelagem Matemática e Computacional) - Instituto de Ciências exatas, Universidade Federal Rural do Rio de Janeiro, Seropédica, 2022.por
dc.identifier.urihttps://rima.ufrrj.br/jspui/handle/20.500.14407/14353-
dc.description.abstractA previsão do consumo de energia elétrica é muito importante para o planejamento energético de um país, empresa ou região. O interesse pelas projeções do consumo de energia elétrica está relacionado, em geral, ao impacto financeiro que a distribuição de energia pode ge- rar, podendo causar imensos prejuízos. Neste trabalho, propomos uma metodologia utilizando a abordagem bottom-up por meio de modelos de séries temporais e análise de cluster para obter a previsão do consumo de energia elétrica. As medidas de eficiência energética foram inseridas na metodologia para avaliar a economia de energia elétrica. Em particular, esta metodologia foi aplicada aos dados de consumo de energia elétrica da Universidade Federal Rural do Rio de Janeiro (UFRRJ). Os resultados mostram que a metodologia apresentou um erro percentual absoluto médio de aproximadamente 1%. Além disso, essa metodologia demostrou um grande potencial para avaliar a implementação de medidas de eficiência energética.por
dc.description.sponsorshipCAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superiorpor
dc.formatapplication/pdf*
dc.languageporpor
dc.publisherUniversidade Federal Rural do Rio de Janeiropor
dc.rightsAcesso Abertopor
dc.subjectConsumo de Energia Elétricapor
dc.subjectSéries Temporaispor
dc.subjectAnálise de Clusterpor
dc.subjectPrevisãopor
dc.subjectMedidas de Eficiência Energéticapor
dc.subjectElectricity Consumptioneng
dc.subjectTime Serieseng
dc.subjectCluster Analysiseng
dc.subjectForecasteng
dc.subjectEnergy Effi- ciency Measureseng
dc.titleUm estudo sobre a previsão do consumo de energia elétrica considerando as medidas de eficiência energética: aplicado à Universidade Federal Rural do Rio de Janeiropor
dc.title.alternativeA study on forecasting electricity consumption considering energy efficiency measures: applied to the Federal Rural University of Rio de Janeiroeng
dc.typeDissertaçãopor
dc.description.abstractOtherElectricity consumption forecasting is very important for the energy planning of a country, com- pany or region. The interest in electricity consumption projections is related, in general, to the financial impact that energy distribution can generate, which can cause immense losses. In this work, we propose a methodology using the bottom-up approach through time series models and cluster analysis to obtain the prediction of electricity consumption. Energy efficiency measures were inserted into the methodology to assess electricity savings. In particular, this methodology was applied to electricity consumption data from the Federal Rural University of Rio de Janeiro (UFRRJ). The results show that the methodology presented an average absolute percentage er- ror of approximately 1%. In addition, this methodology has shown great potential for evaluating the implementation of energy efficiency measures.eng
dc.contributor.advisor1Silva, Felipe Leite Coelho da
dc.contributor.advisor1ID099.619.917-96por
dc.contributor.advisor1IDhttps://orcid.org/0000-0002-7090-5716por
dc.contributor.advisor1Latteshttp://lattes.cnpq.br/9601624302826678por
dc.contributor.referee1Silva, Felipe Leite Coelho da
dc.contributor.referee1ID099.619.917-96por
dc.contributor.referee1IDhttps://orcid.org/0000-0002-7090-5716por
dc.contributor.referee1Latteshttp://lattes.cnpq.br/9601624302826678por
dc.contributor.referee2Vera-Tudela, Carlos Andrés Reyna
dc.contributor.referee2IDhttps://orcid.org/0000-0001-5855-8611por
dc.contributor.referee2Latteshttp://lattes.cnpq.br/6509989261742578por
dc.contributor.referee3Oliveira, Fernando Luiz Cyrino
dc.contributor.referee3IDhttps://orcid.org/0000-0003-1870-9440por
dc.contributor.referee3Latteshttp://lattes.cnpq.br/0348074510343282por
dc.creator.ID106.627.186-07por
dc.creator.Latteshttp://lattes.cnpq.br/7644532697097294por
dc.publisher.countryBrasilpor
dc.publisher.departmentInstituto de Ciências Exataspor
dc.publisher.initialsUFRRJpor
dc.publisher.programPrograma de Pós-Graduação em Modelagem Matemática e Computacionalpor
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Disponível em: http://www.procelinfo.com.br/main.asp?TeamID=%7B82BBD82C-FB89-48CA-98A9- 620D5F9DBD04%7D. Acesso em: 3 fev. 2022.por
dc.subject.cnpqProbabilidade e Estatísticapor
dc.thumbnail.urlhttps://tede.ufrrj.br/retrieve/74691/2022%20-%20Douglas%20Bortolassi%20Filgueiras.pdf.jpg*
dc.originais.urihttps://tede.ufrrj.br/jspui/handle/jspui/6906
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