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. Singlelinkage, 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.

Occupancy profiles modelling based on indoor measurements and clustering analysis: Application in an office building

Dafni Mora
;
Marilena De Simone;Gianmarco Fajilla;
2018

Abstract

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. Singlelinkage, 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.
Occupancy detection, Environmental sensor, Clustering analysis, Office buildings
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11770/274966
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