1. Clustering-based Active Learning on Sensor Type Classification in Buildings Dezhi Hong, Hongning Wang, Kamin Whitehouse University of Virginia 1

2. 2 Costs of A Typical Commercial Building Analytics tools save 10~13% of costs 2 ~$800,000/year 1 2 Schneider Electric Building Report 2013 1 BOMA Kingsley Report 2010

3. 3 How an Analytics Engine Helps 72 o F 86 o F

4. 4 Challenge to Running an Engine Hot Air Temp RMI328 RMI401 Space Temperature Mapping Zone 2 MAT RMI530 Room 530 Mixed Air Temperat ure Room32 8 Hot Air Temperat ure …...

5. 5 Zone1 Temp RMI328 RMI414 Space Temp … SDH_SF1_R282_RMT SODA1R300__ART …...

6. 6 Problem Statement To create the mapping from sensor names in the buildings to the inputs of analytic engines with minimal manual effort

7. 7 Insight -Same Type of Sensors have Similar Names Zone1 Temp RMI328 Label one from each! RMI401 Space Temp Zone2 Temp RMI530 … RMI414 Space Temp … Also similar!

8. 8 Active Learning Selection Algorithm All about minimizing manual labeling effort! A new strategy Reinforce the label to amplify

9. Active learning can reduce manual labeling effort for mapping the sensor names to their types in buildings Hypothesis 9

10. Overview 10 1.Generate clusters on sensors based on similarity in names 2.Select a representative example x from a cluster c for manual labeling y 3.Label the most similar examples to x as y within cluster c

11. Overview: Step I -Generate Clusters 11 RMI414 Space Temp RMI401 Space Temp

12. 12 Overview: Step I -Generate Clusters

13. 13 Classifie r f Overview: Step I -Generate Clusters

14. 14 Classifie r f Overview: Step II -Locate and Label a Representative

15. 15 Classifie r f Size Impurity Overview: Step II -Locate and Label a Representative

16. 16 Overview: Step II -Locate and Label a Representative

17. 17 Overview: Step III -Label Propagation and Sub-clustering

18. 18 Classifie r f Overview: Step III -Label Propagation and Sub-clustering

19. 19 Classifie r f Overview: Step II -Locate and Label a Representative Size Impurity

20. 20 Classifie r f Overview: Step II -Locate and Label a Representative Overview: Step III -Label Propagation and Sub-clustering

21. 21 Classifie r f Overview: Step III -Label Propagation and Sub-clustering

22. Name Feature 22 Zone Temp 2 RMI204 {zone, temp, rmi} {zon, one, tem, emp, rmi}{zon, one, tmp, rmi } (1,1,0,0,1) keep alphabets k-mers: ABCDEFG -> ABC, BCD, CDE… (k=3) frequency count Zone Tmp 1 RMI328

23. Label Propagation Radius Estimation 23

24. 24 Label Propagation Radius Estimation Intra-class pair Inter-class pair

25. 25 Label Propagation Radius Estimation

26. 26 Label Propagation Correction

27. 27 Label Propagation Correction

28. Non-parametric Bayesian Clustering 28

29. Evaluation Dataset From 3 buildings on 2 campuses 2500+ streams 22 types 29 Building ABuilding BBuilding C

30. Baselines 30 Random Least Margin (LM) Pre-clustering (PC): representativeness and uncertainty Hierarchical Clustering (HC): impurity of clusters and size

31. Labeling Effort vs. Accuracy 31 10-fold cross validation (9 for training, 1 for testing) Run 130 iterations for each algorithm Repeat 10 times for each building The average is reported We use a linear SVM

32. Building A 32 Classification Accuracy 1/3 less Labeling Effort vs. Accuracy OUR

33. Effect of Clustering and Label Propagation on Accuracy 33 Clustering Label Propagation (NO Clustering) (NO Propagation)

34. Building A 34 Classification Accuracy Effect of Clustering on Accuracy Original

35. Building A 35 Classification Accuracy Effect of Label Propagation on Accuracy Original

36. 36 A Search Find the sensors in the same room O: Occupancy T: Temperature H: Humidity C: CO2 Only one pattern got discovered

37. Leveraged the patterns in point names Developed a new active learning algorithm Evaluated on a real dataset covering three buildings Our approach requires less labeled examples and enable potential useful applications Conclusion 37

38. Thanks! Questions? 38