Occupancy profiles modelling based on Indoor Measurements and Clustering Analysis: Application in an Office Building
Sensors were applied in an office building to obtain information regarding user presence and absence intervals. Occupancy was also recorded by manual observation, and indoor parameters such as air temperature, relative humidity, carbon dioxide (CO2), volatile organic compounds (VOC) were monitored....
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| Další autoři: | , , |
| Médium: | article |
| Jazyk: | angličtina |
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2018
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| On-line přístup: | https://knepublishing.com/index.php/KnE-Engineering/article/view/1474 http://ridda2.utp.ac.pa/handle/123456789/4379 |
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| _version_ | 1869652478058823680 |
|---|---|
| author | Mora, Dafni |
| author2 | De Simone, Marilena Fajilla, Gianmarco Fábrega, José |
| author2_role | author author author |
| author_browse | De Simone, Marilena Fajilla, Gianmarco Fábrega, José Mora, Dafni |
| author_facet | Mora, Dafni De Simone, Marilena Fajilla, Gianmarco Fábrega, José |
| author_role | author |
| collection | Repositorio Institucional de documento digitales de acceso abierto de la UTP |
| dc.contributor.none.fl_str_mv | |
| dc.creator.none.fl_str_mv | Mora, Dafni De Simone, Marilena Fajilla, Gianmarco Fábrega, José Mora, Dafni De Simone, Marilena Fajilla, Gianmarco Fábrega, José |
| dc.date.none.fl_str_mv | 2018-02-11 2018-02-23T17:18:32Z 2018-02-23T17:18:32Z |
| dc.format.none.fl_str_mv | application/msword application/pdf application/xml |
| dc.identifier.none.fl_str_mv | https://knepublishing.com/index.php/KnE-Engineering/article/view/1474 10.18502/keg.v3i1.1474 http://ridda2.utp.ac.pa/handle/123456789/4379 |
| dc.language.none.fl_str_mv | eng |
| dc.publisher.none.fl_str_mv | KnE Publishing |
| dc.relation.none.fl_str_mv | https://knepublishing.com/index.php/KnE-Engineering/article/view/1474/3284 https://knepublishing.com/index.php/KnE-Engineering/article/view/1474/3551 https://knepublishing.com/index.php/KnE-Engineering/article/view/1474/3552 |
| dc.rights.none.fl_str_mv | info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/ |
| dc.source.none.fl_str_mv | 2518-6841 KnE Engineering; 6th Engineering, Science and Technology Conference - Panama 2017 (ESTEC 2017); 711-720 reponame:Repositorio Institucional de documento digitales de acceso abierto de la UTP instname:Universidad Tecnológica de Panamá instacron:U Tecnológica de Panamá |
| dc.title.none.fl_str_mv | Occupancy profiles modelling based on Indoor Measurements and Clustering Analysis: Application in an Office Building |
| dc.type.none.fl_str_mv | info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
| description | Sensors were applied in an office building to obtain information regarding user presence and absence intervals. Occupancy was also recorded by manual observation, and indoor parameters such as air temperature, relative humidity, carbon dioxide (CO2), volatile organic compounds (VOC) were monitored. Occupants’ behaviors regarding door/window (open/closed) and electric power were considered. Clustering analysis by manual observation was employed to identify similarities in daily or monthly occupancy and to describe possible occupancy profiles. Similar approach was carried out with each monitored parameter and the results of clustering elaboration were compared with the real occupancy profiles to identify which sensor is more effective to measure office occupancy. Furthermore, data were analyzed to explore relationships between occupancy and the magnitude of indoor environmental changes with the objective to identify daily, weekly, or monthly patterns. Single-linkage, complete-linkage, and average-linkage clustering were applied to each dataset. The cophenetic correlation coefficient was used to verify the quality of the results obtained for each variable, and the complete linkage was selected to define the groups. Comparison between occupancy real data clustering and VOC and open/closed door groups demonstrated not similarities. The electricity consumption and CO2 data showed some similarities.Keywords: Occupancy detection, environmental sensor, clustering analysis, Office buildings |
| eu_rights_str_mv | openAccess |
| format | article |
| id | lrtest_ffa7c43f9d642ed1cac137679abd5bcf |
| identifier_str_mv | 10.18502/keg.v3i1.1474 |
| instacron_str | U Tecnológica de Panamá |
| institution | U Tecnológica de Panamá |
| instname_str | Universidad Tecnológica de Panamá |
| language | eng |
| network_acronym_str | lrtest |
| network_name_str | lr |
| oai_identifier_str | oai:ridda2.utp.ac.pa:123456789/4379 |
| publishDate | 2018 |
| publishDateSort | 2018 |
| publisher.none.fl_str_mv | KnE Publishing |
| reponame_str | Repositorio Institucional de documento digitales de acceso abierto de la UTP |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | https://creativecommons.org/licenses/by/4.0/ |
| spelling | Occupancy profiles modelling based on Indoor Measurements and Clustering Analysis: Application in an Office BuildingMora, DafniDe Simone, MarilenaFajilla, GianmarcoFábrega, JoséMora, DafniDe Simone, MarilenaFajilla, GianmarcoFábrega, JoséSensors were applied in an office building to obtain information regarding user presence and absence intervals. Occupancy was also recorded by manual observation, and indoor parameters such as air temperature, relative humidity, carbon dioxide (CO2), volatile organic compounds (VOC) were monitored. Occupants’ behaviors regarding door/window (open/closed) and electric power were considered. Clustering analysis by manual observation was employed to identify similarities in daily or monthly occupancy and to describe possible occupancy profiles. Similar approach was carried out with each monitored parameter and the results of clustering elaboration were compared with the real occupancy profiles to identify which sensor is more effective to measure office occupancy. Furthermore, data were analyzed to explore relationships between occupancy and the magnitude of indoor environmental changes with the objective to identify daily, weekly, or monthly patterns. Single-linkage, complete-linkage, and average-linkage clustering were applied to each dataset. The cophenetic correlation coefficient was used to verify the quality of the results obtained for each variable, and the complete linkage was selected to define the groups. Comparison between occupancy real data clustering and VOC and open/closed door groups demonstrated not similarities. The electricity consumption and CO2 data showed some similarities.Keywords: Occupancy detection, environmental sensor, clustering analysis, Office buildingsKnE Publishing2018-02-112018-02-23T17:18:32Z2018-02-23T17:18:32Zinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/mswordapplication/pdfapplication/xmlhttps://knepublishing.com/index.php/KnE-Engineering/article/view/147410.18502/keg.v3i1.1474http://ridda2.utp.ac.pa/handle/123456789/43792518-6841KnE Engineering; 6th Engineering, Science and Technology Conference - Panama 2017 (ESTEC 2017); 711-720reponame:Repositorio Institucional de documento digitales de acceso abierto de la UTPinstname:Universidad Tecnológica de Panamáinstacron:U Tecnológica de Panamáenghttps://knepublishing.com/index.php/KnE-Engineering/article/view/1474/3284https://knepublishing.com/index.php/KnE-Engineering/article/view/1474/3551https://knepublishing.com/index.php/KnE-Engineering/article/view/1474/3552info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/4.0/oai:ridda2.utp.ac.pa:123456789/43792021-07-06T16:47:39Z |
| spellingShingle | Occupancy profiles modelling based on Indoor Measurements and Clustering Analysis: Application in an Office Building Mora, Dafni |
| status_str | publishedVersion |
| title | Occupancy profiles modelling based on Indoor Measurements and Clustering Analysis: Application in an Office Building |
| title_full | Occupancy profiles modelling based on Indoor Measurements and Clustering Analysis: Application in an Office Building |
| title_fullStr | Occupancy profiles modelling based on Indoor Measurements and Clustering Analysis: Application in an Office Building |
| title_full_unstemmed | Occupancy profiles modelling based on Indoor Measurements and Clustering Analysis: Application in an Office Building |
| title_short | Occupancy profiles modelling based on Indoor Measurements and Clustering Analysis: Application in an Office Building |
| title_sort | Occupancy profiles modelling based on Indoor Measurements and Clustering Analysis: Application in an Office Building |
| url | https://knepublishing.com/index.php/KnE-Engineering/article/view/1474 http://ridda2.utp.ac.pa/handle/123456789/4379 |