An experimental set-up was built in an office with the aim of obtaining information regarding occupancy patterns by monitoring occupancy state, air temperature, relative humidity, CO 2 , VOC, door and window opening, and electricity usage. Heuristic approaches were applied: cluster analysis and models based on logical flowcharts. Cluster analysis was implemented in the ground truth occupancy data to identify daily occupancy patterns by considering different time steps. Clusters marked by daily occupancy lower and greater than 40% were identified. Furthermore, in high occupancy clusters, the analysis distinguished groups in which the day with the highest occupancy was lower or greater than 40%. The same approach was applied with continuous parameters to verify the ability of sensors to replicate the characteristics of each identified cluster. CO 2 and power clusters showed similarities in the number of clusters, days in each cluster, and occupancy percentage. In addition, both continuous and binary variables were used in models based on logical flowcharts to describe hourly occupancy profiles. The best solution with one parameter returned an error of 12%, by using two parameters an error of 10%. Models with three parameters showed errors of less than 10%, accuracy did not improve significantly by adding the fourth parameter.

Occupancy patterns obtained by heuristic approaches: Cluster analysis and logical flowcharts. A case study in a university office

Mora, Dafni;Fajilla, Gianmarco;De Simone, Marilena
2019

Abstract

An experimental set-up was built in an office with the aim of obtaining information regarding occupancy patterns by monitoring occupancy state, air temperature, relative humidity, CO 2 , VOC, door and window opening, and electricity usage. Heuristic approaches were applied: cluster analysis and models based on logical flowcharts. Cluster analysis was implemented in the ground truth occupancy data to identify daily occupancy patterns by considering different time steps. Clusters marked by daily occupancy lower and greater than 40% were identified. Furthermore, in high occupancy clusters, the analysis distinguished groups in which the day with the highest occupancy was lower or greater than 40%. The same approach was applied with continuous parameters to verify the ability of sensors to replicate the characteristics of each identified cluster. CO 2 and power clusters showed similarities in the number of clusters, days in each cluster, and occupancy percentage. In addition, both continuous and binary variables were used in models based on logical flowcharts to describe hourly occupancy profiles. The best solution with one parameter returned an error of 12%, by using two parameters an error of 10%. Models with three parameters showed errors of less than 10%, accuracy did not improve significantly by adding the fourth parameter.
Cluster analysis; Heuristic; Occupancy; Occupancy modeling; Office buildings; Sensors; Civil and Structural Engineering; Building and Construction; Mechanical Engineering; Electrical and Electronic Engineering
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11770/292252
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