1. Occupancy data analytics and prediction: A case study
Xin Liang, Tianzhen Hong, Geoffrey Qiping Shen
Presented by: Debarun Das

2. Outline Background Applications Methodology Case Study Conclusions
Machine Learning Algorithms
Case Study
Conclusions
Further Study
Outline

3. Background Occupant Presence Modelled By: Fixed Schedules
Occupants categorized into groups
Early Bird, Timetable complier, Flexible worker
Each group assigned to a specific schedule
Occupant presence satisfies a probability distribution
Binomial Distribution, Poisson Distribution etc
Analyzing Practical Observation Data
Limited to single or a few offices
T. Zhang, P.-O. Siebers, U. Aickelin, Modelling electricity consumption in office
D. Wang, C.C. Federspiel, F. Rubinstein, Modeling occupancy in single person
K. Sun, D. Yan, T. Hong, S. Guo, Stochastic modeling of overtime occupancy and its application in building energy simulation and calibration offices,

4. https://irail.be/spitsgids
Applications
Train Occupancy Prediction
iRail - Spitsgids from Belgium
Uses Crowdsourced data for Occupancy prediction
Hospital Occupancy Prediction
Time Series data
Statistical models of prediction
Control Of HVAC systems
Logistic Regression Model
Reduces Electricity Consumption
Improves User Comfort
Steven J. Littig & Mark W. Isken, «Short term hospital occupancy prediction”
Jie Shia, Nanpeng Yua, Weixin Yaob, “Energy efficient building HVAC control algorithm with real-time occupancy prediction”

5. Methodology

6. Machine Learning Algorithms
Unsupervised
Clustering Algorithm- k means
Supervised
Decision Tree Learning - Algorithm C4.5

7. k-means Discovers patterns of occupancy schedule
Needs a predefined value of k
Needs a predefined distance definition
Chooses among:
Euclidean Distance
Correlation Similarity
Dynamic Time Wrap

8. k-means - Choosing k and Distance Metric
Davies-Bouldin Index
𝐷𝐵𝐼= 1 𝑛 𝑖=1 𝑛 max 𝑗≠ 𝜎 𝑖 +𝜎 𝑗 𝑑 𝑐 𝑖 , 𝑐 𝑗
Optimal Parameters
k = 4
Distance Metric = Euclidean

9. Decision Tree Summarizes rules within the patterns
The attribute to split is chosen by Information Gain, 𝐺𝑎𝑖𝑛 𝑆, 𝐴
𝐺𝑎𝑖𝑛 𝑆, 𝐴 =𝐻 𝑆 − 𝑣∈𝑉𝑎𝑙𝑢𝑒𝑠(𝐴) | 𝑆 𝑣 | |𝑆| 𝐻( 𝑆 𝑣 )
where 𝐻 𝑆 = − 𝑝 𝑖 log 2 𝑝 𝑖

10. Decision Tree - Example

12. Case Study Building 101 in the Navy Yard, Philadelphia
Four sensors installed at the gates of the building
Records no. of Occupants Entering and Exiting the building
𝑁 𝑡𝑜𝑡𝑎𝑙 = 1 𝑖 (𝑁 𝑖1 − 𝑁 𝑖2 + 𝑁 𝑖3 − 𝑁 𝑖4 + 𝑁 𝑖5 − 𝑁 𝑖6 + 𝑁 𝑖7 − 𝑁 18 )
Uses Matlab and RapidMiner

13. General Characteristics of Occupant Presence
Low occupant presence on weekends and holidays
Excludes weekend and holiday data
High Variance of data from 7am to 4pm
Stochastic and highly variable

14. General Characteristics of Occupant Presence
Night (7pm - 6am)
Going-to-Work (7am – 9am)
Morning (10am – 12pm)
Noon-break (12 pm – 1pm)
Afternoon (2pm – 3pm)
Going-home (4pm – 6pm)

15. Patterns of Occupant Presence
Occupancy Rate
Working Time
Going To Work
Going To Home
Noon Break
Pattern 1
Lowest
Shortest
Latest
Earliest NA
Pattern 2
Highest
Longest
Later
12 pm
Pattern 3
Medium
2 pm
Pattern 4
Earlier
1 pm

16. Rules of Patterns 3 influencing factors Seasons (temperatures)
Weekdays
Daylight Saving Time (DST)
DST cannot contribute to enough information gain

19. Prediction of Occupancy Schedule
Method 1
𝑃𝑟𝑒𝑑𝑖𝑐𝑡𝑖𝑜𝑛 𝑡 = 𝑀 𝑑𝑎𝑦 𝑡
Method 2
𝑃𝑟𝑒𝑑𝑖𝑐𝑡𝑖𝑜𝑛 𝑤𝑒𝑒𝑘𝑑𝑎𝑦,𝑡 = 𝑀 𝑤𝑒𝑒𝑘𝑑𝑎𝑦 𝑡
Method 3
𝑃𝑟𝑒𝑑𝑖𝑐𝑡𝑖𝑜𝑛 𝑑𝑎𝑦,𝑡 = 𝑀 𝑝1 . 𝑃 𝑝1 + 𝑀 𝑝2 . 𝑃 𝑝2 + 𝑀 𝑝3 . 𝑃 𝑝3 + 𝑀 𝑝4 . 𝑃 𝑝4

20. Validation 𝑅𝑀𝑆𝐸= 𝑖=1 𝑛 ( 𝐸 𝑖 − 𝐸 𝑖 ) 2 𝑛 𝑀𝐴𝐸= 𝑖=1 𝑛 | 𝐸 𝑖 − 𝐸 𝑖 | 𝑛
𝑅𝑀𝑆𝐸= 𝑖=1 𝑛 ( 𝐸 𝑖 − 𝐸 𝑖 ) 2 𝑛
𝑀𝐴𝐸= 𝑖=1 𝑛 | 𝐸 𝑖 − 𝐸 𝑖 | 𝑛
𝑚𝑒𝑑𝐸=𝑚𝑒𝑑𝑖𝑎𝑛( 𝐸 𝑖 − 𝐸 𝑖 )

23. Conclusions High accuracy in prediction of occupant behavior
Simple input data
Relatively Simple Algorithms
Lower Complexity compared to learning algorithms like Neural Nets
Simple Prediction method – weighted mean
Can be applied for control of energy consumption
Accuracy of occupancy detection procedure is not discussed in details
Does not compare against other learning algorithms

24. Further Readings
Usman Habib, Gerhard Zucker, “Automatic occupancy prediction using unsupervised learning in buildings data”
James Scott , A.J. Bernheim Brush, John Krumm, Brian Meyers, Mike Hazas, Steve Hodges, Nicolas Villar, “PreHeat: Controlling Home Heating Using Occupancy Prediction”

25. Thank you!