1. Progress Report Presenter : Min-chia Chang Advisor : Prof. Jane Hsu Date : 2011 - 03 - 01

2. Outline  Prediction of AC State (revised)  Definition of AC Waste Analysis  Result of AC Waste Analysis  Thesis – Chapter 2 2011/03/012NTU CSIE iAgent Lab

3. Outline  Prediction of AC State (revised)  Definition of AC Waste Analysis  Result of AC Waste Analysis  Thesis – Chapter 2 2011/03/013NTU CSIE iAgent Lab

4. Data -label: ( OFF, ON no green, ON green ) define  y={0,1,2} -OFF : close -ON no green : T indoor < T userSetting, valve = OFF -ON green : T indoor > T userSetting, valve = ON -feature : define  ｘ, which is a vector -(T indoor, H indoor, T vent, H vent, T outdoor, H outdoor ) -Context data 2011/03/014NTU CSIE iAgent Lab

5. Place -R104_1, R104_2, R104_3, R104_4 (classroom) -R204_1, R204_2, R204_3, R204_4, R204_5, R204_6 (computer classroom) -R318_1(professor room) -R324_1, R324_2 (seminar room) -R336_1, R336_2 (lab) -R439_1 (seminar room) -R521_1, R521_2 (seminar room) 2011/03/015NTU CSIE iAgent Lab zone note: each zone contains only one AC controller

6. Dataset  D={( ｘ n,y n )}, where n=1 to N -each minute of labeled period (original : intersection of vent and indoor) -labeled by camera (original : controlled on purpose by duck) -size = 77,439 2010/12/016NTU CSIE iAgent Lab

7. Dataset  Time -R336 : 2010-12-18 ~ 2011-01-06 -R204 : 2011-01-06 ~ 2011-01-17 -R324 : 2011-01-20 ~ 2011-01-30 2011/03/017NTU CSIE iAgent Lab

8. Execution environment  Weka  Function: SVM -Kernel: RBF  Cross Validation: 3-fold -In each iteration : 2011/03/018NTU CSIE iAgent Lab Dataset Training Data Testing Data note : NEVER use testing data before you predict.

9. Accuracy  Baseline - ｘ =(T vent ) -accuracy = 72.66% -time = - ｘ =(T indoor, H indoor, T vent, H vent, T outdoor, H outdoor ) -accuracy = 93.21% -time = - ｘ =( T indoor, H indoor, T vent, H vent, T outdoor, H outdoor, ……) 2011/03/019NTU CSIE iAgent Lab

10. Bagging (bootstrap aggregation) 2011/03/0110NTU CSIE iAgent Lab Dataset Training Data Testing Data K=?, S=? K fixed - If S decreases, then time decreases. S fixed - If K increases, then the result of the vote is more convinced. …… K training data size = S re-sampling

11. Bagging 2011/03/0111NTU CSIE iAgent Lab …… K training data size = S Training Data re-sampling y=0y=1 y=2 S/3 K fixed - If S decreases, then time decreases.

12. K=?, S=? 2011/03/0112NTU CSIE iAgent Lab x= 6dimensions K=1K=10K=30K=50 S=30063.14% 16s 74.78% 2m4s 80.4% 6m10s 79.69% 10m18s S=150085.37% 30s 87.90% 4m20s 88.96% 13m16s 89.20% 22m8s S=300088.27% 47s 90.48% 7m7s 91.03% 21m36s 91.08% 38m07s S=1500091.93% 6m29s 92.52% 31m11s S=3000092.55% 19m18s baseline:93.21% 45m12s time && accuracy => trade-off

13. Generate feature  x =( T indoor, H indoor, T vent, H vent, T outdoor, H outdoor, some context data) 2011/03/0113NTU CSIE iAgent Lab context datadimensionsvalue chilled water host3{0,1} chilled water temperature1integer rotation speed of pump1float new or old (building)2{0,1} floor5{0,1} room type6{0,1} area1float day of the week7{0,1} weekday or weekend2{0,1} semester or vacation2{0,1} hour of the day24{0,1}

14. 2011/03/0114NTU CSIE iAgent Lab x= 60dimensio ns K=1K=5K=10K=30K=50 S=30058.32% 18s 78.11% 1m23s 76.03% 2m41s 79.38% 7m31s 83.26% 13m12s S=150084.82% 39s 89.12% 3m12s 88.84% 6m18s 91.27% 18m12s 91.46% 31m42s S=300090.41% 1m34s 92.33% 7m23s 93.21% 13m18s 93.60% 40m14s 93.59% 1h03m50s S=1500094.72% 14m30s 95.38% 1h12m55s S=3000095.43% 39m40s baseline:96.11% 1h32m50s K=?, S=?

15. Process the missing value  missing value : T indoor, H indoor, T vent, H vent  processing method :  method 1 : encoding  e.g. : (?, ?, 15.2, ?) => (0, ?, 0, ?, 1, 15.2, 0, ?)  method 2 : interpolation (linear)  e.g. : 2011-01-31 23:50 : (20, 45, 10, 70) 2011-01-31 23:51 : (?, 45.2, 9.9, 70.2) …… 2011-02-01 00:00 : (20.1,45.5,10.2,69.2) => ? = 20.01  method 3 : encoding + interpolation 2011/03/0115NTU CSIE iAgent Lab

16. Result 2011/03/0116NTU CSIE iAgent Lab baselinebagging(K=30, S=3000) (T vent ) 72.66% 11m34s 70.43% 6 dimentions 93.21% 45m12s 91.03% 21m36s + generate features 96.11% 1h32m50s 93.40% 40m14s + missing value (encode) 96.07% 1h13m43s 93.43% + missing value (interpolation) 99.79% 54m41s 98.58% + missing value (encode + interpolation) 99.60% 1h33m40s 96.10% + normalize 97.55%94.05%

17. Outline  Prediction of AC State (revised)  Definition of AC Waste Analysis  Result of AC Waste Analysis  Thesis – Chapter 2 2011/03/0117NTU CSIE iAgent Lab

18. Problem definition : energy(AC) waste analysis  Input : -state of motion sensor, m n ={no, yes} -AC information  Output : -proportion of AC waste 2011/03/0118NTU CSIE iAgent Lab

19. System overview 2011/03/01 19 NTU CSIE iAgent Lab motion sensor state motion sensor state AC state predictor AC state predictor thermal comfort questionnaire thermal comfort questionnaire T indoor T outdoor thermal comfort equation and offset thermal comfort equation and offset AC information input output proportion of AC waste proportion of AC waste analysis AC waste analysis

20. Condition of AC waste state of motion sensor m n state of AC y n T indoor ? T comfortableRange waste or not N0higherN N0amongN N0lowerN N1higherY N1amongY N1lowerY N2higherY N2amongY N2lowerY Y0higherN Y0amongN Y0lowerN Y1higherN?? Y1amongN Y1LowerN?? Y2HigherY Y2AmongN Y2lowerY 2011/03/0120NTU CSIE iAgent Lab

21. Outline  Prediction of AC State (revised)  Definition of AC Waste Analysis  Result of AC Waste Analysis  Thesis – Chapter 2 2011/03/0121NTU CSIE iAgent Lab

22.  Currently, I encountered a problem here …… 2011/03/0122NTU CSIE iAgent Lab

23. Problem 2011/03/0123NTU CSIE iAgent Lab AC state predictor AC state predictor AC information collector AC information collector Dataset Another testing data Some place we did not have in the dataset yet. Some patterns of the feature’s combination (dependent on time) in another testing data haven’t seen in the dataset. error prediction 分 vacation non-vacation 2011-01-01~2011-01-31 all 2010-12-17~2011-01-30 R336 R324 R204

24. Current condition  feature : -T indoor, H vent -new or old (building) -floor -room type -area bagging -S=30000 -K=5 2011/03/0124NTU CSIE iAgent Lab

25. Condition of AC waste  waste situation 1.m n = no and (y n = 1 or y n = 2) 2.m n = yes and y n = 2 and T indoor < T comfortableRange 3.m n = yes and y n = 2 and T indoor > T comfortableRange 2011/03/0125NTU CSIE iAgent Lab

26. Proportion of AC waste  waste situation 1.m n = no and (y n = 1 or y n = 2) 2.m n = yes and y n = 2 and T indoor < T comfortableRange 3.m n = yes and y n = 2 and T indoor > T comfortableRange 2011/02/2126NTU CSIE iAgent Lab placem n =nom n =yesy n =0y n =1y n =2waste 1waste 2waste 3 336_258%42%26%61%13%36.7%9.5%0% 204_147%53%23%70%7%33.8%4.0%0% 204_243%57%19%39%42%35.8%17.8%0% 204_348%52%53%44%3%23.9%16.7%0% 204_457%43%53%41%6%28.2%4.0%0% 204_565%35%15%13%72%57.2%20.8%0% 204_665%35%55%41%5%31.9%2.3%0% 20433%67%7%--31.0%-- 2011.01 204_T ： 只用１，２的 ｖｏｔｅ 找出浪費的時 間週期 ……

27. Outline  Prediction of AC State (revised)  Definition of AC Waste Analysis  Result of AC Waste Analysis  Thesis – Chapter 2 2011/03/0127NTU CSIE iAgent Lab

28. backup 2010/10/1428NTU CSIE iAgent Lab

29. 3. 問題定義 - 第一部分 2011/01/08 29 NTU CSIE iAgent Lab 空調狀態 預測機 空調狀態 預測機 空調資訊 收集器 空調資訊 收集器 出風口溫度 收集器 出風口溫度 收集器 室內溫度 收集器 室內溫度 收集器 室外溫度 收集器 室外溫度 收集器 舒適比例 分析中心 舒適比例 分析中心 熱舒適溫度公式 空調設定 收集器 空調設定 收集器 有 / 無人資訊 收集器 有 / 無人資訊 收集器