1. The Building Adapter: Towards Quickly Applying Building Analytics at Scale Dezhi Hong, Hongning Wang, *Jorge Ortiz, Kamin Whitehouse University of Virginia, *IBM Research 1

2. 2

3. 3 Challenge to Running an Engine Hot Water Temp RMI328 RMI401 Space Temperature Zone 2 MAT RMI530 Room 530 Mixed Air Temperat ure Room32 8 Hot Water Temperat ure......

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

5. 5 Challenge to Running an Engine Hot Water Temp RMI328 RMI401 Space Temperature Zone 2 MAT RMI530 Room 530 Mixed Air Temperat ure Room32 8 Hot Water Temperat ure......

6. 6

7. 7 Hot Water Temp RMI328 RMI401 Space Temperature Zone 2 MAT RMI530 Room 530 Mixed Air Temperat ure Room32 8 Hot Water Temperat ure......

8. 8

9. 9

10. Transfer learning can eliminate manual labeling from mapping the point names to their types in buildings Hypothesis 10

11. 11 Insight Labeled SourceUnlabeled Target Zone1 Temp RMI328 Zone2 Temp RMI304...... SDH_SF1_R282_RMT SDH_SF2_R517_RMT......

12. 12 Transfer Learning Labeled SourceUnlabeled Target SDH_SF1_R282_RMT Probably a mistake!

13. 13 Source BuildingTarget Building f1f1 f2f2 ….. Step I: Encapsulate Knowledge from Source

14. 14 Source BuildingTarget Building f1f1 Step II: Clustering on Names in Target Building f2f2

15. 15 Step III: Weighted Sum Prediction Larger weight! Source BuildingTarget Building f1f1 f2f2

16. 16 Step III: Weighted Sum Prediction Source BuildingTarget Building f1f1 f2f2 Larger weight!

17. Data Feature 17 Min, Max, … MIN = [min 1, min 2, …, min N ] F = [ min(MIN), max(MIN), median(MIN), var(MIN)... ] 12…N

18. Name Feature 18 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

19. Classifier Weighting 19 Classifier 1Classifier 2

20. Classifier Weighting 20 # of Common Examples Total # of Unique Examples Classifier 1Classifier 2 w Sim = 5/5 2/5

21. Thresholding on Weight 21 # of Common Examples Total # of Unique Examples Classifier 1Classifier 2 Sim = Sim > delta

22. Non-parametric Bayesian Clustering 22

23. Evaluation Dataset 3 buildings on 2 campuses 2700+ points 22 types 7 days data 23 Building ABuilding BBuilding C

24. Mapping Accuracy and Coverage Train on building A and test on building B Run on three pairs of buildings Repeat with different weight thresholds Classifiers - Random Forest, Logistic Regression and SVM Metrics - Coverage - Accuracy 24

25. Empirically, a threshold around 0.4 can strike a balance btw Acc and Cov 25 Percentag e Mapping Accuracy (Acc) and Coverage (Cov)

26. Transfer from A to C 26

27. Combining the two Approaches 27 Combo: start with fully automated, then switch to active learning AL Only: simply run active learning AL Only

28. Combining Multiple Buildings as Source More Sources, More Promising! 28

29. More buildings as source Customized data features Better weighting function What level of accuracy needed for analytics Discussion 29

30. 30 Related Work Minimizes manual effort within a building Bharttacharya et. al – BuildSys’15 Gao et. al – BuildSys’15 Schumann et. al – BuildSys’14 Hong et. al – CIKM’15

31. Leveraged the complementary attributes of sensors Developed techniques to automatically map point names Experimental results on three buildings show the promise of approach Conclusion 31

32. Thanks Questions? 32