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 2NTU CSIE iAgent Lab

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

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

5. 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 It consumes about 40.88% of the total power consumption (source : NTU 校園數位電錶監視系統 ) 5

6. Architecture of Central A/C System  Chilled water host Evaporator Condenser  Other devices Pump Cooling tower 6NTU CSIE iAgent Lab

7. Control of Central A/C System 7NTU 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

8. Energy Conservation for Central A/C System 8NTU 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 )

9. Abnormal A/C State in NTU CSIE  Ideal power consumption 9NTU CSIE iAgent Lab KWH

10. Abnormal A/C State in NTU CSIE  Real power consumption 10NTU CSIE iAgent Lab KWH

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

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

13. System Overview 13NTU CSIE iAgent Lab

14. Wireless Sensor Network 14NTU CSIE iAgent Lab

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

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

17. Collection Unit 17NTU 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 18NTU CSIE iAgent Lab  One server per floor (1F to 5F)  Relays deployed around the corridors  Room Class room : R104 Computer class room : R204 Professor room : R318 Seminar room : R324, R439, R521 Laboratory : R336

19. A/C Mode Recognition 19NTU CSIE iAgent Lab

20. 20NTU CSIE iAgent Lab

21. A/C Mode Recognition  Goal : using machine learning to train the model for recognizing the A/C mode  Input : environmental data  Output : A/C mode ∈ {off, venting, cooling}  Mode Off mode : blower= off Venting mode : blower = on, valve = off Cooling mode : blower= on, valve = on 21NTU CSIE iAgent Lab

22. Dataset PeriodPlace November 2010R104, R204, R318, R324, R336, R439, R521 December 2010 - January 2011R204, R324, R336 February 2010 - March 2011R104, R204 22NTU 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%

23. Feature Extraction 23NTU CSIE iAgent Lab  Temperature and humidity Indoor Vent Outdoor  Delta (temperature and humidity)  Parameters of the central A/C system  Spatial

24. Preprocessing  Missing data treatment Encoding : recognize the data is missing or not Linear interpolation : all missing data are temperature and humidity Exception : if the first or last data is missing data, replaced with global mean after the interpolation  Normalization Min-max normalization : [0,1] It prevents features with large scale biasing the result 24NTU CSIE iAgent Lab

25. Experiment Setting  Execution environment : weka  Learning algorithm : SVM Kernel function : RBF  Scenario 1.4-fold cross validation 2.The outdoor weather pattern in testing data doesn’t exist in training data (Constraint : we can’t collect all the outdoor weather patterns in real environment.) 25NTU CSIE iAgent Lab

26. Steps of the Experiment 2 26NTU CSIE iAgent Lab outdoor temperature outdoor humidity  Clustering the dataset Algorithm : k-means (k=4) Feature: outdoor temperature, outdoor humidity Color : outdoor weather pattern  4-fold cross validation

27. 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) 27NTU 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%

28. Thermal Comfort Calculation 28NTU CSIE iAgent Lab

29. Thermal Comfort Calculation  GOAL : Find thermal comfort range of the environment  INPUT : Questionnaire  OUTPUT : Thermal comfort range 29NTU CSIE iAgent Lab

30. 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 30NTU CSIE iAgent Lab

31. 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 31NTU CSIE iAgent Lab ScaleThermal sensation +3Hot +2Warm +1Slightly warm 0Neutral Slightly cool -2Cool -3Cold

32. 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] 32NTU CSIE iAgent Lab

33. 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} 33NTU CSIE iAgent Lab VALID !

34. Data Collection R204 (computer class room)R336 (laboratory) PeriodMarch 2010 - July 2010December 2010 - February 2011 Number1,7451,033 34NTU 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%)

35. Result 35NTU 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)

36. 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) 36NTU CSIE iAgent Lab

37. Thermal Comfort Range 37NTU CSIE iAgent Lab  Regression Indoor temperature Mean thermal sensation vote (PMV) during each ℃ 2.67

38. Thermal Comfort Range 38NTU CSIE iAgent Lab 2.67

39. A/C State Evaluation 39NTU CSIE iAgent Lab

40. 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 40NTU CSIE iAgent Lab

41. A/C State 41NTU 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

42. Outline  Introduction  System  Analysis  Conclusion 42NTU CSIE iAgent Lab

43. Analysis of Abnormal A/C States 43NTU CSIE iAgent Lab Abnormal A/C States Detecting System Abnormal A/C States Detecting System normal/ abnormal Analysis User useful information

44. Valid Data  From January 2011 to May 2011 44NTU 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%

45. Professor Room - R318 45NTU 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

46. Seminar Room – R439 46NTU 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

47. Class Room – R104 47NTU 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

48. Computer Class Room – R204 48NTU 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

49. Class Room – R336 49NTU 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

50. R204 and R336 50NTU 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

51. Outline  Introduction  System  Analysis  Conclusion 51NTU CSIE iAgent Lab

52. Conclusion and Contribution  Collect the environmental data in NTU CSIE for more than five months  Build a SVM model to recognize the A/C mode for each zone  Calculate the regression line and the range of the thermal comfort in NTU CSIE from questionnaires  The proposed system is useful in detecting abnormal A/C states and providing analysis to modify user behaviors 52NTU CSIE iAgent Lab

53. Future Work  Improve the quality of the wireless sensor network Modify the architecture of the sensor network Auto repair  Use persuasive technology to provide the analytic results for users  Recognizing the activity level for reducing the power consumption of A/C 53NTU CSIE iAgent Lab

54. Thank You Q & A 54NTU CSIE iAgent Lab

55. 2010/10/14 55NTU CSIE iAgent Lab

56. 2010/10/14 56NTU CSIE iAgent Lab

57. Thermal Comfort Range 57NTU CSIE iAgent Lab

58. A/C State 58NTU 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

59. analysis 2 2010/10/14 59NTU CSIE iAgent Lab 用電量 高溫下 用電群 高溫下 用電群 中溫下 用電群 中溫下 用電群 低溫下 用電群 低溫下 用電群 室外溫度 標記 = 高用電 標記 = 中用電 標記 = 低用電 用電量 同上

60. Goal :  Method 1 : powerConsumption, state_0, state_1, state_2, state_3 @attribute state {L0,L1,L2,L3,L4…,L9}  Method 2 : powerConsumption, R0S0,R0S1,R0S2,R0S3,R1S0,R1S1,……,R6S0,R6S1,R6S2,R6S3  Method 3 : powerconsumption,R0,R1,R2,R3,R4,R5,R6 @attribute R {S0,S1,S2,S3} 目前結果 : 無法推出不正常使用空調狀態與高用電量之關係 原因 : 用電量為 136 間總量, 只 sample 其中 7 間 (1 間 ), 未 sample 到的房間之影響不知如何 2010/10/14 60NTU CSIE iAgent Lab

61. 將每小時歸為某種天氣型態 ( 三群 ) 低溫中溫高溫 Outdoor AVG 14.0 (8.0 – 16.9)19.8(16.9 – 22.8)25.8(22.8 – 34.1) # of each clustering1621(45%)1286(35%)717(20%) 2010/10/14 61NTU CSIE iAgent Lab

62. 每種天氣型態之用電量分群 ( 三群 ) 2010/10/14 62NTU CSIE iAgent Lab 低溫 Label = 低用電 Label = 中用電 Label = 高用電 Power Consumption 131.0 (107.5 – 132.9)134.9(132.9 – 137.0)139.0(137.0 – 145.2) # of each clustering 233(37%)302(48%)100(16%) 中溫 Label = 低用電 Label = 中用電 Label = 高用電 Power Consumption134.9(128.8 – 138.4)142.0(138.5 – 146.9)152.7(147.4 – 169.3) # of each clustering81(28%)130(44%)82(28%) 高溫 Label = 低用電 Label = 中用電 Label = 高用電 Power Consumption145.8 (133.3 – 150.8)156.3 (151.1 – 163.1)170.8 (164.0 – 187.4) # of each clustering52(37%)47(34%)41(29%)

63. R324 63NTU 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

64. R521 64NTU 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