The smart thermostat: using occupancy sensors to save energy in homes

By Jiakang Lu, Tamim I. Sookoor, Vijay Srinivasan, Ge Gao, B. Holben, J. Stankovic, E. Field, Kamin Whitehouse. ACM International Conference on Embedded Networked Sensor Systems, 2010

594 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/cd3992771fabe5870791d9647846125d1c340fe4

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. M. A. Carreira-Perpiñán
2. Alberto E. Cerpa
3. M. Avci
4. Luc Van Gool
5. Jitendra Malik
6. Trevor Darrell
7. Alan L. Yuille
8. Antonio Torralba

In Electrical and Electronic Engineering

1. M. A. Carreira-Perpiñán
2. Alberto E. Cerpa
3. M. Avci
4. Eleonora Borgia

Citations in the corpus

• Demand Response-Enabled Model Predictive HVAC Load Control in Buildings using Real-Time Electricity Pricing (2013, methodology): “In order to evaluate this approach, they installed sensors in 8 homes and compared the expected energy usage of their algorithm against existing approaches (Lu et al., 2010).”
• OBSERVE: Occupancy-based system for efficient reduction of HVAC energy (2011, methodology): “We found SCOPES was able to detect 80% of all recorded transitions within a 24 hour period of time.”
• The Internet of Things vision: Key features, applications and open issues (2014, background): “Other types of applications are integrated with the smart grid and optimize the domestic consumption [134].”