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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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Autores Principales: Mora, Dafni, De Simone, Marilena, Fajilla, Gianmarco, Fábrega, José
Formato: Artículo
Idioma: Inglés
Publicado: KnE Publishing 2018
Acceso en línea: https://knepublishing.com/index.php/KnE-Engineering/article/view/1474
http://ridda2.utp.ac.pa/handle/123456789/4379
id RepoUTP4379
recordtype dspace
spelling RepoUTP43792021-07-06T16:47:39Z Occupancy profiles modelling based on Indoor Measurements and Clustering Analysis: Application in an Office Building Mora, Dafni De Simone, Marilena Fajilla, Gianmarco Fábrega, José Mora, Dafni De Simone, Marilena Fajilla, Gianmarco Fábrega, José 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 2018-02-11 2018-02-23T17:18:32Z 2018-02-23T17:18:32Z info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion https://knepublishing.com/index.php/KnE-Engineering/article/view/1474 10.18502/keg.v3i1.1474 http://ridda2.utp.ac.pa/handle/123456789/4379 eng 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 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/ application/msword application/pdf application/xml KnE Publishing 2518-6841 KnE Engineering; 6th Engineering, Science and Technology Conference - Panama 2017 (ESTEC 2017); 711-720
institution Universidad Tecnológica de Panamá
collection Repositorio UTP – Ridda2
language Inglés
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
format Artículo
author Mora, Dafni
De Simone, Marilena
Fajilla, Gianmarco
Fábrega, José
Mora, Dafni
De Simone, Marilena
Fajilla, Gianmarco
Fábrega, José
spellingShingle Mora, Dafni
De Simone, Marilena
Fajilla, Gianmarco
Fábrega, José
Mora, Dafni
De Simone, Marilena
Fajilla, Gianmarco
Fábrega, José
Occupancy profiles modelling based on Indoor Measurements and Clustering Analysis: Application in an Office Building
author_sort Mora, Dafni
title 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_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_sort occupancy profiles modelling based on indoor measurements and clustering analysis: application in an office building
publisher KnE Publishing
publishDate 2018
url https://knepublishing.com/index.php/KnE-Engineering/article/view/1474
http://ridda2.utp.ac.pa/handle/123456789/4379
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score 12.043273

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