1. 智慧型節能：使用感測網路自動偵測異常空調 狀態之研究 Intelligent Sensing for Energy Saving : A Case Study on Detecting Abnormal Air-Conditioning States Using A Sensor Network Presenter : Min-Chia Chang Advisor : Prof. Jane Hsu Date : 2011-06 -30

2. Outline  Introduction  System  Analysis  Conclusion 2 NTU CSIE iAgent Lab

3. Energy Saving 3 NTU CSIE iAgent Lab  Reason  Policy

4. Power Consumption in a Building 4 NTU CSIE iAgent Lab (source : Continental Automated Buildings Association, CABA)

5. Architecture of Central A/C System  Chilled water host Evaporator Condenser  Other devices Pump Cooling tower 5 NTU CSIE iAgent Lab

6. Energy Conservation for Central A/C System 6 NTU CSIE iAgent Lab  Device setting The setting of the chiller water [Zhao, Enertech Engineering Company] Parameter optimization of the cooling tower [James and Frank 2010]  Building automation system Component Energy saving controller Infrared motion sensor (source : NTU 電機學系 )

7. Power Consumption in NTU CSIE  Total 9,036.4 KWH/day ≒ 28,012 NTD/day ( January 2009 - April 2011 ) (source : NTU 校園數位電錶監視系統 )  Central A/C system ( July 2010 - April 2011 ) 3,693.8 KHW/day 40.88% of the total (source : NTU 校園數位電錶監視系統 ) 7

8. Abnormal A/C State in NTU CSIE  Ideal power consumption 8 NTU CSIE iAgent Lab KWH

9. Abnormal A/C State in NTU CSIE  Real power consumption 9 NTU CSIE iAgent Lab KWH A/C is turned off ?

10. Control of Central A/C System 10 NTU CSIE iAgent Lab  Central Chilled water host Off mode On mode (All year on duty)  Local A/C controller Off mode Venting mode Cooling mode

11. Abnormal A/C State in NTU CSIE 11 NTU CSIE iAgent Lab HotCold

12. Outline  Introduction  System  Analysis  Conclusion 12 NTU CSIE iAgent Lab

13. System Overview 13 NTU CSIE iAgent Lab

14. Wireless Sensor Network 14 NTU CSIE iAgent Lab

15. Sensors  Platform : Taroko Temperature and humidity sensor : SHT 11 Infrared motion sensor 15 NTU CSIE iAgent Lab

16. Nodes in the Sensor Network 16 NTU CSIE iAgent Lab  Sender (temperature, humidity, ID) (preamble, motion value, ID)  Receiver Data saving : 1 minute  Relay

17. Collection Unit 17 NTU CSIE iAgent Lab  Room : divide into zones according to A/C controller  Environmental data temperature and humidity vent indoor occupancy state indoor vent motion sensor

18. Deployment 18 NTU CSIE iAgent Lab  One server per floor (1F to 5F)  Relays deploy around the corridors

19. Deployment  Room Class room : R104 Computer class room : R204 Professor room : R318 Laboratory : R336 Seminar room : R324, R439, R521 19 NTU CSIE iAgent Lab

20. A/C Mode Recognition 20 NTU CSIE iAgent Lab

21. A/C Mode  Mode Off mode : blower= off Venting mode : blower = on, valve = off Cooling mode : blower= on, valve = on 21 NTU CSIE iAgent Lab wind velocity wind velocity A/C mode A/C mode temperature setting temperature setting A/C power Off Venting Cooling indoor temperature ≧ ＜

22. A/C Mode Recognition  GOAL : Using machine learning to build the model for recognizing the A/C mode  ASSUMPTION : People control the A/C mode part of according to the weather  INPUT : Feature vector  OUTPUT : A/C mode 22 NTU CSIE iAgent Lab

23. Features 23 NTU CSIE iAgent Lab CategoryFeatureDimensionType Temperature and Humidity T I, H I, T V, H V, T O, H O 6Float Delta ΔT I,V, ΔH I,V ΔT I,O, ΔH I,O ΔT O,V, ΔH O,V 6Float Parameters (Central A/C ) Host3{0,1} Leaving Temperature1Float Rotation Speed of Pump1Float Spatial Building2{0,1} Floor5{0,1} Room Type5{0,1} Area1Float

24. Annotation  Method 1 Control on purpose  Method 2 Record by camera 24 NTU CSIE iAgent Lab PeriodPlaceAnnotation November 2010R104, R204, R318, R324, R336, R439, R521Method1 December 2010 - January 2011 R204, R324, R336Method2 February 2010 - March 2011 R104, R204Method2

25. Dataset 25 NTU CSIE iAgent Lab PlaceTotal DataLabel = off Label = venting Label = cooling Missing Data 204_117,3456,3716,8174,1574,68627.0% 204_217,1066,5698,1682,3694,60326.9% 204_316,6946,3575,3814,9564,57627.4% 204_416,4158,5925,0142,8094,63228.2% 204_517,4876,569010,9186,05434.6% 204_615,6166,7945,8432,9799,15858.7% 336_220,8896,43910,1004,3505,93128.4%

26. Evaluation  Each zone builds a model 1.4-fold cross validation 2.Constraint : couldn’t collect all the weather patterns The weather pattern in testing data doesn’t exist in training data 26 NTU CSIE iAgent Lab

27. Steps of the Experiment 2 27 NTU CSIE iAgent Lab outdoor temperature outdoor humidity  Cluster Algorithm : k-means (k=4) Feature: outdoor temperature, outdoor humidity  Leave-one-out Cross Validation 336_2

28. Preprocessing  Missing data treatment Encoding Recognize the data is missing or not Linear interpolation All missing data are temperature and humidity If the first or last data is missing data Replace with global mean after the interpolation  Normalization Min-max normalization : [0,1] It prevents features with large scale biasing the result 28 NTU CSIE iAgent Lab

29. Experiment Result  Result Each zone’s accuracy in experiment 2 is higher than 85% Each zone’s accuracy in experiment 1 is higher than experiment 2 204_5 has the highest accuracy (only 2 label) 29 NTU CSIE iAgent Lab ZoneExperiment 1Experiment 2 204_198.6%87.0% 204_289.1%85.0% 204_399.8%98.4% 204_498.0%90.2% 204_599.9%99.0% 204_693.9%86.3% 336_293.3%92.1%

30. Thermal Comfort Calculation 30 NTU CSIE iAgent Lab

31. Thermal Comfort Calculation  GOAL : Find thermal comfort range of the environment  INPUT : Questionnaire  OUTPUT : Thermal comfort range 31 NTU CSIE iAgent Lab

32. PMV  Predicted Mean Vote model [Fanger 1970] Calculated analytically by 6 factors : [-3, +3] Metabolic rate Clothing insulation Air temperature Radiant temperature (Outdoor temperature) Relative humidity Air velocity 32 NTU CSIE iAgent Lab

33. Thermal Sensation Scale  Thermal sensation scale [ASHRAE Standard 55] Adaptive method to get PMV Constraints Metabolic rate : 1.0Met - 2.0Met Clothing insulation : ≦ 1.5 Clo Comfortable or not -1, 0, +1 : yes -2, -3, +2, +3 : no 33 NTU CSIE iAgent Lab ScaleThermal sensation +3Hot +2Warm +1Slightly warm 0Neutral Slightly cool -2Cool -3Cold

34. Thermal Comfort - Linear Regression  Field survey Collect thermal sensation vote (TSV) Outdoor temperature has the highest relevance with thermal comfort 1.T C = 17.8 + 0.31T O (Worldwide) [deDear and Brager 1998] 2.T C = 18.3 + 0.158T O (Hong Kong) [Mui and Chan 2003] 3.T C = 15.5 + 0.29T O (Taiwan) [Lin et al. 2008] 34 NTU CSIE iAgent Lab

35. Questionnaire  Thermal sensation scale : {-3, -2, -1, 0,+1, +2, +3}  Direct question : {comfortable, not comfortable}  Metabolic rate : {after sport, static activity}  Clothing insulation : {sleeveless, shirt-sleeve, long-sleeve, thick coat} 35 NTU CSIE iAgent Lab VALID !

36. Data Collection R204 (computer class room)R336 (laboratory) PeriodMarch 2010 - July 2010December 2010 - February 2011 Number1,7451,033 36 NTU CSIE iAgent Lab -3-20+1+2+3 Comfortable55 (46%) 10 (24%) 283 (86%) 1604 (98%) 308 (70%) 39 (43%) 16 (14%) Not Comfortable 65 (54%) 32 (76%) 48 (14%) 34 (2%) 131 (30%) 52 (57%) 101 (86%)

37. Result 37 NTU CSIE iAgent Lab  Linear regression equation T C = 20.6+ 0.107T O 1.T C = 17.8 + 0.31T O (Worldwide) 2.T C = 18.3 + 0.158T O (Hong Kong) 3.T C = 15.5 + 0.29T O (Taiwan)

38. PMV - PPD  Predicted of Percentage Dissatisfied model [Olesen and Bragen 2004] Typical standard : 80% acceptability, (PMV, PPD)= (±0.85, 20) Higher standard : 90% acceptability, (PMV, PPD)= (±0.50, 10) 38 NTU CSIE iAgent Lab

39. Thermal Comfort Range 39 NTU CSIE iAgent Lab  Regression Indoor temperature Mean thermal sensation vote (PMV) during each ℃ 2.67

40. Thermal Comfort Range 40 NTU CSIE iAgent Lab 2.67

41. A/C State Evaluation 41 NTU CSIE iAgent Lab

42. A/C State Evaluation  GOAL : Classify the room’s A/C state to normal or abnormal  INPUT : Each zone Occupancy state A/C mode Indoor temperature Thermal comfort range  OUTPUT : A/C state 42 NTU CSIE iAgent Lab

43. A/C State 43 NTU CSIE iAgent Lab people in the room A/C = turned on Y N Y N normal abnormal A/C = cooling mode Y N normal indoor temperature ? comfort range indoor temperature ? comfort range lowerhigherwithin normal abnormal

44. Outline  Introduction  System  Analysis  Conclusion 44 NTU CSIE iAgent Lab

45. Analysis of Abnormal A/C States 45 NTU CSIE iAgent Lab Abnormal A/C States Detecting System Abnormal A/C States Detecting System normal/ abnormal history data analysis history data analysis User useful information

46. Target Room  Room Class room : R104 Computer class room : R204 Professor room : R318 Laboratory : R336 Seminar room : R324, R439, R521 46 NTU CSIE iAgent Lab

47. Valid Data  From January 2011 to May 2011 47 NTU CSIE iAgent Lab PlaceJanuaryFebruaryMarchAprilMay R10436,52582%33,04582%36,95883%34,07679%6,07214% R20439,13588%15,40138%28,12363%31,16772%13,95831% R31833,44475%32,05379%35,74280%31,97874%34,80678% R32430.99369%26,72266%29,89067%24,97858%29,21265% R33635,27779%28,87272%43,08897%39,92092%40,60491% R43939,68189%24,28460%35,21279%30,65871%34,15877% R521386,5987%34,92787%38,17186%26,99262%18,65742%

48. Professor Room - R318 48 NTU CSIE iAgent Lab State(April 2011) Percentage 0 : no people but AC not closed(abnormal)2,201 (6.9%) 1 : too cold (abnormal)242 (0.8%) 2 : too hot (abnormal) 1 (0%) 3 : others (normal)29,534 (92.4% ) weekday weekend distribution during a week

49. Seminar Room – R439 49 NTU CSIE iAgent Lab State(April 2011) Percentage 0 : no people but AC not closed (abnormal) 2,650 (8.6%) 1 : too cold (abnormal) 897 (2.9%) 2 : too hot (abnormal) 0 (0.0%) 3 : others (normal) 27,111 (88.4%) weekday weekend distribution during a week

50. Class Room – R104 50 NTU CSIE iAgent Lab State(April 2011) Percentage 0 : no people but AC not closed (abnormal) 628 (1.8%) 1 : too cold (abnormal) 3,062 (9.0%) 2 : too hot (abnormal) 1 (0%) 3 : others (normal) 30,386 (89.2%) weekday weekend distribution during a week

51. Computer Class Room – R204 51 NTU CSIE iAgent Lab State(April 2011) Percentage 0 : no people but AC not closed (abnormal) 5,582 (17.9%) 1 : too cold (abnormal) 18,080 (58.0%) 2 : too hot (abnormal) 14 (0%) 3 : others (normal) 7,491 (24.0%) weekday weekend distribution during a week

52. Class Room – R336 52 NTU CSIE iAgent Lab State(April 2011) Percentage 0 : no people but AC not closed (abnormal) 15,559 (39.0%) 1 : too cold (abnormal) 0 (0.0%) 2 : too hot (abnormal) 5,360 (13.4%) 3 : others (normal) 19,001 (47.6%) weekday weekend distribution during a week

53. R204 and R336 53 NTU CSIE iAgent Lab  R204 State 1 takes up a big percentage in every month  R336 State 0 takes up a big percentage in every month When the weather became warmer, state 2 would happen more frequently

54. Outline  Introduction  System  Analysis  Conclusion 54 NTU CSIE iAgent Lab

55. Conclusion and Contribution  Collect the environmental data in NTU CSIE continuously  Build the SVM model to recognize the A/C mode  Find the thermal comfort range of NTU CSIE  The proposed system is useful in detecting abnormal A/C states and helping users be aware of incorrect behaviors 55 NTU CSIE iAgent Lab

56. Future Work  Improve the quality of the wireless sensor network  Use persuasive technology to provide the results for users  Recognize the activity level of NTU CSIE in each time interval 56 NTU CSIE iAgent Lab

57. Thank You Q & A 57 NTU CSIE iAgent Lab

58. 2010/10/14 58 NTU CSIE iAgent Lab

59. 標記 2010/12/09 59 NTU CSIE iAgent Lab 地點時間 時間長度 ( 分鐘 ) R10411/14 00:00 – 11/15 17:272487 R11311/12 14:17 – 11/15 17:214504 R20411/13 11:43 – 11/15 10:352812 R31811/13 11:34 – 11/15 17:223228 R32411/13 12:33 – 11/15 10:342761 R33611/12 08:47 – 11/15 11:454498 R43911/13 11:30 – 11/15 10:332823 R51311/12 14:27 – 11/15 14:234316 R52111/14 14:55 – 11/15 17:301595

60. 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 2010/12/01 60 NTU CSIE iAgent Lab

61. 2010/10/14 61 NTU CSIE iAgent Lab

62. 62 NTU CSIE iAgent Lab context dataindexdimensionsValue 室內溫 / 濕度 (raw)1-22Float 出風口溫 / 濕度 (raw)3-42Float 室外溫 / 濕度 5-62Float 冰水主機 7-93{0,1} 出水溫度 101Integer 泵浦轉速 111Float 舊館 / 新館 12-132{0,1} 樓層 14-185{0,1} 房間類型 19-246{0,1} 區域編號 25-306{0,1} 建積 311float 星期幾 32-387{0,1} 周間 / 周末 39-402{0,1} 學期中 / 寒暑假 41-422{0,1} 小時 43-6624{0,1} 室內溫 / 濕度 (Interpolation)67-682Float 出風口溫 / 濕度 (Interpolation)69-702Float 室內溫 / 濕度 (Encode)71-722Float 出風口溫 / 濕度 (Encode)73-742Float 差距 ( 室內, 出風口 )75-762Float 差距 ( 室內, 室外 )77-782Float 差距 ( 出風口, 室外 )79-802Float

63. Cross-Validation : 3-Fold 63 NTU CSIE iAgent Lab T (V) TH(V)TH(V), TH(I)TH(V), TH(I), TH(O) TH(V), TH(I), TH(O), AC host, AC degree, AC speed 204_10.680.800.840.960.98 204_20.700.850.860.970.99 204_30.680.820.830.97 204_40.710.840.880.970.98 204_50.87 0.880.970.99 204_60.600.630.810.960.98 336_20.87 0.960.98 Avg.0.770.810.850.970.98

64. Thermal Comfort Range 64 NTU CSIE iAgent Lab

65. A/C State 65 NTU CSIE iAgent Lab  Abnormal No people in the room but there exists at least one zone’s AC not closed People in the room and there exists at least one zone where the AC is cooling mode and cooling below lower bound of the comfort range People in the room and there exists at least one zone where the AC is cooling mode but warmer above upper bound of the comfort range  Normal Other states

66. R324 66 NTU CSIE iAgent Lab EventR104_T 0: 不正常 ( 無人, 空調開啟 ) 3.6% (898) 1: 不正常 ( 有人, 空調開啟且過冷 ) 5.5% (1381) 2: 不正常 ( 有人, 空調開啟且過熱 ) 0% (0) 3: 正常 ( 其他使用情形 ) 90.9% (22699) weekday weekend distribution during a week

67. R521 67 NTU CSIE iAgent Lab EventR104_T 0: 不正常 ( 無人, 空調開啟 ) 5.9% (1594) 1: 不正常 ( 有人, 空調開啟且過冷 ) 4.4% (1196) 2: 不正常 ( 有人, 空調開啟且過熱 ) 0.0% (4) 3: 正常 ( 其他使用情形 ) 89.6% (24198) weekday weekend distribution during a week