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

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

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

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

Online WalkSense Method

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

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 |

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

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

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

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

Evaluation – Inference Accuracy

Evaluation – Implementation Accuracy

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

Sensitivity Analysis Number of Sensors

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

Sensitivity Analysis Behavior Patterns

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

Sensitivity Analysis The value of 𝑘

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

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?

Questions? Thank you