Using diverse sensors in load forecasting in an office building to support energy management
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.22/18418 |
Resumo: | The increasing penetration of renewable energy sources led to the development of several energy management approaches. One of the main topics in this field is related to the load forecast in buildings, which can contribute to more intelligent and sustainable energy consumption. However, it is necessary to build a proper forecast model, capable of detecting an accurate consumption profile. The minimum effort to achieve this is to extract a historic with energy consumptions to use as input. Additional information should be considered in order to achieve improvements in forecasting results. This way, information regarding the day of the week is discussed as a reliable source of information that may enhance the load forecast. In this paper, two forecasting techniques, namely neural networks and support vector machine, are used to predict the energy consumption of a building for all 5 min from a period. The proposed model finds the best forecasting technique and determines if the additional information regarding the day of the week enhances the load forecast. In this case study, a period of two years and a half data with a 5-minute time interval is used. Moreover, several tests are performed for varied inputs to understand if the insights are consistent for these tests. This data has been adapted from an office building to illustrate the advantages of the proposed methodology. |
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Using diverse sensors in load forecasting in an office building to support energy managementClusteringData miningFuzzy C-meansTypical load profileUnsupervised learningThe increasing penetration of renewable energy sources led to the development of several energy management approaches. One of the main topics in this field is related to the load forecast in buildings, which can contribute to more intelligent and sustainable energy consumption. However, it is necessary to build a proper forecast model, capable of detecting an accurate consumption profile. The minimum effort to achieve this is to extract a historic with energy consumptions to use as input. Additional information should be considered in order to achieve improvements in forecasting results. This way, information regarding the day of the week is discussed as a reliable source of information that may enhance the load forecast. In this paper, two forecasting techniques, namely neural networks and support vector machine, are used to predict the energy consumption of a building for all 5 min from a period. The proposed model finds the best forecasting technique and determines if the additional information regarding the day of the week enhances the load forecast. In this case study, a period of two years and a half data with a 5-minute time interval is used. Moreover, several tests are performed for varied inputs to understand if the insights are consistent for these tests. This data has been adapted from an office building to illustrate the advantages of the proposed methodology.This work has received funding from FEDER Funds through COMPETE program and from National Funds through (FCT) under the projects UIDB/00760/2020, MAS-Society (PTDC/EEI-EEE/28954/2017), CEECIND/02887/2017, and SFRH/BD/144200/2019, and from ANI (project GREEDi).ElsevierRepositório Científico do Instituto Politécnico do PortoRamos, DanielTeixeira, BrígidaFaria, PedroGomes, LuisAbrishambaf, OmidVale, Zita2021-09-17T14:21:05Z20202020-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.22/18418eng2352-484710.1016/j.egyr.2020.11.100info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-03-13T13:09:42Zoai:recipp.ipp.pt:10400.22/18418Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:37:52.673265Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Using diverse sensors in load forecasting in an office building to support energy management |
title |
Using diverse sensors in load forecasting in an office building to support energy management |
spellingShingle |
Using diverse sensors in load forecasting in an office building to support energy management Ramos, Daniel Clustering Data mining Fuzzy C-means Typical load profile Unsupervised learning |
title_short |
Using diverse sensors in load forecasting in an office building to support energy management |
title_full |
Using diverse sensors in load forecasting in an office building to support energy management |
title_fullStr |
Using diverse sensors in load forecasting in an office building to support energy management |
title_full_unstemmed |
Using diverse sensors in load forecasting in an office building to support energy management |
title_sort |
Using diverse sensors in load forecasting in an office building to support energy management |
author |
Ramos, Daniel |
author_facet |
Ramos, Daniel Teixeira, Brígida Faria, Pedro Gomes, Luis Abrishambaf, Omid Vale, Zita |
author_role |
author |
author2 |
Teixeira, Brígida Faria, Pedro Gomes, Luis Abrishambaf, Omid Vale, Zita |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Repositório Científico do Instituto Politécnico do Porto |
dc.contributor.author.fl_str_mv |
Ramos, Daniel Teixeira, Brígida Faria, Pedro Gomes, Luis Abrishambaf, Omid Vale, Zita |
dc.subject.por.fl_str_mv |
Clustering Data mining Fuzzy C-means Typical load profile Unsupervised learning |
topic |
Clustering Data mining Fuzzy C-means Typical load profile Unsupervised learning |
description |
The increasing penetration of renewable energy sources led to the development of several energy management approaches. One of the main topics in this field is related to the load forecast in buildings, which can contribute to more intelligent and sustainable energy consumption. However, it is necessary to build a proper forecast model, capable of detecting an accurate consumption profile. The minimum effort to achieve this is to extract a historic with energy consumptions to use as input. Additional information should be considered in order to achieve improvements in forecasting results. This way, information regarding the day of the week is discussed as a reliable source of information that may enhance the load forecast. In this paper, two forecasting techniques, namely neural networks and support vector machine, are used to predict the energy consumption of a building for all 5 min from a period. The proposed model finds the best forecasting technique and determines if the additional information regarding the day of the week enhances the load forecast. In this case study, a period of two years and a half data with a 5-minute time interval is used. Moreover, several tests are performed for varied inputs to understand if the insights are consistent for these tests. This data has been adapted from an office building to illustrate the advantages of the proposed methodology. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020 2020-01-01T00:00:00Z 2021-09-17T14:21:05Z |
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/10400.22/18418 |
url |
http://hdl.handle.net/10400.22/18418 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2352-4847 10.1016/j.egyr.2020.11.100 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Elsevier |
publisher.none.fl_str_mv |
Elsevier |
dc.source.none.fl_str_mv |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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