Real-time indoor air quality (IAQ) monitoring system for smart buildings

Detalhes bibliográficos
Autor(a) principal: Biondo, Elias
Data de Publicação: 2023
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10198/28332
Resumo: Mestrado de dupla diplomação com a UTFPR - Universidade Tecnológica Federal do Paraná
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spelling Real-time indoor air quality (IAQ) monitoring system for smart buildingsIndoor air qualityMonitoring systemMachine learningArtificial intelligenceDomínio/Área Científica::Engenharia e Tecnologia::Outras Engenharias e TecnologiasMestrado de dupla diplomação com a UTFPR - Universidade Tecnológica Federal do ParanáIndoor air quality (IAQ) is a term describing the air quality of a room, it refers to the health and comfort of the occupants. Normally, people spend around 90% of their time in indoor environments where the concentration of air pollutants, such CO, CO2, VOCs, SO2, O3 and NOx, may be two to five times — and occasionally, more than 100 times — higher than outdoor levels. According to the World Health Organization (WHO), the indoor air pollution is responsible for the deaths of 3.8 million people annually. It has been indicated that IAQ in residential areas or buildings is significantly affected by three primary factors: (i) Outdoor air quality, (ii) human activity in buildings, and (iii) building and construction materials, equipment, and furniture. In this contest, this work consist in a real time IAQ system to monitoring and control thermal comfort and gas concentration. The system has a data acquisition stage, where the data is measured by a set of sensors and then stored on InfluxDB database and displayed in Grafana. To track the behavior of the measured parameters, two machine learning algorithms are developed, a mathematical model linear regression, and an artificial intelligence model neural network. In a test made to see how precise were the prediction of the two models, linear regression model performed better then neural network, presenting cases of up to 99.7% and 98.1% of score prediction, respectively. After that, a test with smoke was done to validate the models where the results shows that both learning models can detect adverse cases. Finally, prediction data are storage on InfluxDB and displayed on Grafana to monitoring in real-time measured data and prediction data.Lima, JoséBrito, ThadeuNakano, Alberto YoshihiroBiblioteca Digital do IPBBiondo, Elias2023-05-19T13:51:28Z20232023-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10198/28332TID:203299701enginfo: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:RCAAP2023-11-21T11:02:12Zoai:bibliotecadigital.ipb.pt:10198/28332Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T23:18:23.119454Repositório 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 Real-time indoor air quality (IAQ) monitoring system for smart buildings
title Real-time indoor air quality (IAQ) monitoring system for smart buildings
spellingShingle Real-time indoor air quality (IAQ) monitoring system for smart buildings
Biondo, Elias
Indoor air quality
Monitoring system
Machine learning
Artificial intelligence
Domínio/Área Científica::Engenharia e Tecnologia::Outras Engenharias e Tecnologias
title_short Real-time indoor air quality (IAQ) monitoring system for smart buildings
title_full Real-time indoor air quality (IAQ) monitoring system for smart buildings
title_fullStr Real-time indoor air quality (IAQ) monitoring system for smart buildings
title_full_unstemmed Real-time indoor air quality (IAQ) monitoring system for smart buildings
title_sort Real-time indoor air quality (IAQ) monitoring system for smart buildings
author Biondo, Elias
author_facet Biondo, Elias
author_role author
dc.contributor.none.fl_str_mv Lima, José
Brito, Thadeu
Nakano, Alberto Yoshihiro
Biblioteca Digital do IPB
dc.contributor.author.fl_str_mv Biondo, Elias
dc.subject.por.fl_str_mv Indoor air quality
Monitoring system
Machine learning
Artificial intelligence
Domínio/Área Científica::Engenharia e Tecnologia::Outras Engenharias e Tecnologias
topic Indoor air quality
Monitoring system
Machine learning
Artificial intelligence
Domínio/Área Científica::Engenharia e Tecnologia::Outras Engenharias e Tecnologias
description Mestrado de dupla diplomação com a UTFPR - Universidade Tecnológica Federal do Paraná
publishDate 2023
dc.date.none.fl_str_mv 2023-05-19T13:51:28Z
2023
2023-01-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10198/28332
TID:203299701
url http://hdl.handle.net/10198/28332
identifier_str_mv TID:203299701
dc.language.iso.fl_str_mv eng
language eng
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.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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