1. Presented by Xiaoyu (Veronica) Liang
Practical Occupancy Detection for Programmable and Smart Thermostats Elahe Soltanaghaei, Kamin Whitehouse published in: 2017 Applied Energy

2. Occupancy Detection Whether a space is occupied or not at given time?
How many people are in the given space at given time?
What events/behaviors do the occupants have? …
Three main occupancy states:
Active
Sleep
Away

3. Walkway Sensing Sensing the walkways to the occupancy zones
𝑆: Sleep Walkway – must have a non-empty area not covered by 𝐴 or 𝑂
𝑂: Outside Walkway – must have a non- empty area not covered by 𝐴 or 𝑆
𝐴: Active – must cover people who are not in the bedroom or outside at least once during their presence in the zone

4. Offline WalkSense Method
𝐾: the minimum duration of a sleep or away state (60 ~ 150 minutes)
∄ 𝑡 𝑝 |(𝑝∈𝑂∪𝐴)⋀( 𝑡 𝑠𝑖 < t p < t sj )
𝑡 𝑠𝑖 and 𝑡 𝑠𝑗 could be two different sleep walkway sensors

5. Online WalkSense Method

6. Implementation Standard Motion Sensor Back-to-Back Implementation
Intermittent sleep/away intervals

7. Implementation Standard Motion Sensor
A candidate sleep/away interval 𝑡 𝑠𝑖 , 𝑡 𝑠𝑗 is defined as ambiguous if
The ambiguous sleep interval 𝑡 𝑠𝑖 , 𝑡 𝑠𝑗 will be defined as sleep if
∋( 𝑡 𝑎𝑖 , 𝑡 𝑎𝑗 )⋀( 𝑡 𝑎𝑖+1 , 𝑡 𝑎𝑗+1 )| 𝑡 𝑎𝑖 < 𝑡 𝑠𝑖 < 𝑡 𝑎𝑗 ⋀ 𝑡 𝑠𝑗 < 𝑡 𝑎𝑖+1 ⋁( 𝑡 𝑎𝑖+1 < 𝑡 𝑠𝑗 < 𝑡 𝑎𝑗+1 ⋀ 𝑡 𝑠𝑖 > 𝑡 𝑎𝑖 )
𝑡 𝑠𝑗 − 𝑡 𝑠𝑖 <| 𝑡 𝑎𝑖+1 − 𝑡 𝑎𝑗 |

8. Implementation Standard Motion Sensor Back-to-Back Implementation
Intermittent sleep/away intervals

9. Experimental Setup No special instructions are given to participants
Students, professionals, and homemakers
Ground truth – manual daily reports of the residents

10. Fraction of time that O are wrongly detected as A.
Evaluation Metrics
Total number of experiment days
Energy Penalty = 1 𝑑 𝑖=1 𝑑 ( 𝑡 𝑖 𝑂𝑎𝑠𝐴 + 𝑡 𝑖 𝑆𝑎𝑠𝐴 + 𝑡 𝑖 𝑂𝑎𝑠𝑆 )
Comfort Penalty = 1 𝑑 𝑖=1 𝑑 ( 𝑡 𝑖 𝐴𝑎𝑠𝑂 + 𝑡 𝑖 𝐴𝑎𝑠𝑆 + 𝑡 𝑖 𝑆𝑎𝑠𝑂 )
Detection Rate = 𝑖=1 𝑁 𝑡 𝑖 𝑆𝑎𝑠𝑆 + 𝑡 𝑖 𝑂𝑎𝑠𝑂 + 𝑡 𝑖 𝐴𝑎𝑠𝐴 𝑡 𝑖 𝑡𝑜𝑡𝑎𝑙
Miss-Interval Rate (MIR) = 𝑐𝑜𝑢𝑛𝑡 𝐼 𝑓 𝑆𝑎𝑠𝐴 +𝑐𝑜𝑢𝑛𝑡( 𝐼 𝑓 𝑂𝑎𝑠𝐴 ) 𝑁 𝑓 + 𝑀 𝑓
The number of fragmented sleep intervals that are wrongly detected as active
The total number of fragmented sleep and outside instances

11. Evaluation -- Energy vs. Comfort Penalty
Offline WalkSense
Online WalkSense

12. Evaluation – Inference Accuracy

13. Evaluation – Implementation Accuracy

14. Sensitivity Analysis Number of Sensors The Training Size
Behavior Patterns
Number of Occupants
The Value of 𝑘
Sensitivity to Intermittent Events

15. Sensitivity Analysis Number of Sensors

16. Sensitivity Analysis Number of Sensors The Training Size
Behavior Patterns
Number of Occupants
The value of 𝑘
Sensitivity to Intermittent Events

17. Sensitivity Analysis
Behavior Patterns

18. Sensitivity Analysis Number of Sensors The Training Size
Behavior Patterns
Number of Occupants
The value of 𝑘
Sensitivity to Intermittent Events

19. Sensitivity Analysis
The value of 𝑘

20. Sensitivity Analysis Number of Sensors The Training Size
Behavior Patterns
Number of Occupants
The value of 𝑘
Sensitivity to Intermittent Events

21. Conclusions Proposed an occupancy detection by using Walkway sensing
Differentiate different zone occupancy states as sleep, active, and away
Achieve good performance
A practical application?
Coarse zoning?
Energy saving?
How to set up HVAC system according to the occupancy information?
Comfort level for occupants?

22. Questions? Thank you