NDLI: Cross-Space Building Occupancy Modeling by Contextual Information Based Learning

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Cross-Space Building Occupancy Modeling by Contextual Information Based Learning

Content Provider	ACM Digital Library
Author	Becerik-Gerber, Burcin Yang, Zheng
Abstract	Research on building occupancy modeling is becoming increasingly important and prevalent, as occupancy is closely related to building energy efficiency, lighting control, security monitoring, emergency evacuation, and rescue operations. Building occupancy is time-sequenced occupancy changes that represent how occupants occupy a building, including both occupant presence and occupant number. Extensive research has been carried out to model building occupancy based on ambient sensing. However, occupancy models reported in previous work are mostly used to detect and estimate the occupancy in the same spaces, where the models are trained, and the generalizability of existing occupancy models to different spaces has not been explored. In general, collecting precise and continuous occupancy data for each space for training can be time consuming and intrusive. In most cases, it might not be realistic to have access to actual occupancy for all of the spaces in a building, thus it is difficult to establish an individual model for each space and improve large-scale occupancy awareness at the building level. We present a systematic framework for cross-space occupancy modeling through non-intrusive ambient sensing and relationship learning, by which the model was trained in one space and then applied to other geometrically similar spaces. Six rooms in two buildings were selected to validate the effectiveness of the framework. It was found that daily F-measure ranged from 0.71 to 0.94 with the mean of 0.80, and daily RMSE ranged from 0.42 to 1.72 with the mean of 1.23 for all classes of occupancy. Daily occupied/unoccupied detection accuracy ranged from 0.79 to 0.99 with the mean of 0.91; and the daily number estimation accuracy ranged from 0.63 to 0.78 with the mean of 0.71, when only the occupied periods were considered. The results showed that the F-measure and daily RMSE had an approximately negative linear relationship while the daily occupied/unoccupied detection accuracy had an approximately positive logarithmic relationship with the daily number estimation accuracy. Three potential influential factors on cross-modeling performance were also investigated. It was found that the level of real-time occupancy variation, the degree of long-term occupancy difference between spaces, and the operation of HVAC systems had negatively logarithmic, linear, and logistic relations with the daily cross-space occupancy modeling accuracies, respectively.
Starting Page	177
Ending Page	186
Page Count	10
File Format	PDF
ISBN	9781450339810
DOI	10.1145/2821650.2821668
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-11-04
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Building occupancy Ambient sensing Contextual information Cross-space modeling Presence detection Number estimation
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
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