TY - GEN
T1 - WalkSense
T2 - 3rd ACM Conference on Systems for Energy-Efficient Built Environments, BuildSys 2016
AU - Soltanaghaei, Elahe
AU - Whitehouse, Kamin
N1 - Publisher Copyright:
© 2016 ACM.
PY - 2016/11/16
Y1 - 2016/11/16
N2 - Home automation systems can save a huge amount of energy by detecting home occupancy and sleep patterns to automatically control lights, HVAC, and water heating. However, the ability to achieve these benefits is limited by a lack of sensing technology that can reliably detect zone occupancy states. We present a new concept called Walkway Sensing based on the premise that motion sensors are more reliable in walkways than occupancy zones, such as hallways, foyers, and doorways, because people are always moving and always visible in walkways. We present a methodology for deploying motion sensors and a completely automated algorithm called WalkSense to infer zone occupancy states. WalkSense can operate in both offline (batch) and online (real-time) mode. We evaluate our system on 350 days worth of data from 6 houses. Results indicate that WalkSense achieves 96% and 95% average accuracies in offline and online modes, respectively, which translates to over 47% and 30% of reduced energy wastage, and 71% and 30% of reduced comfort issues per day, in comparison to the conventional offline and online approaches.
AB - Home automation systems can save a huge amount of energy by detecting home occupancy and sleep patterns to automatically control lights, HVAC, and water heating. However, the ability to achieve these benefits is limited by a lack of sensing technology that can reliably detect zone occupancy states. We present a new concept called Walkway Sensing based on the premise that motion sensors are more reliable in walkways than occupancy zones, such as hallways, foyers, and doorways, because people are always moving and always visible in walkways. We present a methodology for deploying motion sensors and a completely automated algorithm called WalkSense to infer zone occupancy states. WalkSense can operate in both offline (batch) and online (real-time) mode. We evaluate our system on 350 days worth of data from 6 houses. Results indicate that WalkSense achieves 96% and 95% average accuracies in offline and online modes, respectively, which translates to over 47% and 30% of reduced energy wastage, and 71% and 30% of reduced comfort issues per day, in comparison to the conventional offline and online approaches.
KW - Motion sensor
KW - Occupancy detection
KW - Walkway sensing
UR - http://www.scopus.com/inward/record.url?scp=85006746640&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85006746640&partnerID=8YFLogxK
U2 - 10.1145/2993422.2993576
DO - 10.1145/2993422.2993576
M3 - Conference contribution
AN - SCOPUS:85006746640
T3 - Proceedings of the 3rd ACM Conference on Systems for Energy-Efficient Built Environments, BuildSys 2016
SP - 167
EP - 176
BT - Proceedings of the 3rd ACM Conference on Systems for Energy-Efficient Built Environments, BuildSys 2016
PB - Association for Computing Machinery
Y2 - 15 November 2016 through 17 November 2016
ER -