Abstract
Saving energy in residential and commercial buildings is of great interest due to diminishing resources. Heating ventilation
and air conditioning systems, and electric lighting are responsible for a significant share of energy usage, which makes it desirable
to optimise their operations while maintaining user comfort. Such optimisation requires accurate occupancy estimations. In contrast
to current, often invasive or unreliable methods we present an approach for accurate occupancy estimation using a wireless sensor network (WSN) that only collects non-sensitive data and a novel, hierarchical analysis method. We integrate potentially uncertain
contextual information to produce occupancy estimates at different levels of granularity and provide confidence measures for effective building management. We evaluate our framework in real-world deployments and demonstrate its effectiveness and accuracy for occupancy monitoring in both low- and high-traffic area scenarios. Furthermore, we show how the system is used for analysing historical data and identify effective room misuse and thus a potential for energy saving.
and air conditioning systems, and electric lighting are responsible for a significant share of energy usage, which makes it desirable
to optimise their operations while maintaining user comfort. Such optimisation requires accurate occupancy estimations. In contrast
to current, often invasive or unreliable methods we present an approach for accurate occupancy estimation using a wireless sensor network (WSN) that only collects non-sensitive data and a novel, hierarchical analysis method. We integrate potentially uncertain
contextual information to produce occupancy estimates at different levels of granularity and provide confidence measures for effective building management. We evaluate our framework in real-world deployments and demonstrate its effectiveness and accuracy for occupancy monitoring in both low- and high-traffic area scenarios. Furthermore, we show how the system is used for analysing historical data and identify effective room misuse and thus a potential for energy saving.
Original language | English |
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Title of host publication | BuildSys’14 - Proceedings of the 1st ACM Conference on Embedded Systems for Energy-Efficient Buildings |
Editors | Dane Christensen, Xiaofan Jiang |
Place of Publication | New York NY USA |
Publisher | Association for Computing Machinery (ACM) |
Pages | 90-99 |
Number of pages | 10 |
ISBN (Electronic) | 9781450331449 |
DOIs | |
Publication status | Published - 2014 |
Externally published | Yes |
Event | ACM Conference on Embedded Systems for Energy-Efficient Buildings 2014 - Memphis, United States of America Duration: 3 Nov 2014 → 6 Nov 2014 Conference number: 1st http://www.buildsys.org/2014/ |
Conference
Conference | ACM Conference on Embedded Systems for Energy-Efficient Buildings 2014 |
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Abbreviated title | BuildSys’14 |
Country/Territory | United States of America |
City | Memphis |
Period | 3/11/14 → 6/11/14 |
Internet address |
Keywords
- Algorithms
- Experimentation
- Occupancy estimation
- Hierarchical modeling
- Environmental sensing
- Energy