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 :

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

Energy Saving 3 NTU CSIE iAgent Lab  Reason  Policy

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

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

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 )

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

Control of Central A/C System 8 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

 Ideal A/C power consumption Assumption : people number ∝ A/C power consumption Abnormal A/C State in NTU CSIE 9 NTU CSIE iAgent Lab KWH

Abnormal A/C State in NTU CSIE  Real A/C power consumption From electricity meter 10 NTU CSIE iAgent Lab KWH A/C is turned off ? 20KWH

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

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

System Overview 13 NTU CSIE iAgent Lab

Wireless Sensor Network 14 NTU CSIE iAgent Lab

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

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

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

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

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

A/C Mode Recognition 20 NTU CSIE iAgent Lab

A/C Mode  Mode Off mode : blower= off, valve = 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 ≧ ＜

A/C Mode Recognition  GOAL : Using machine learning to build the model for recognizing the A/C mode  INPUT : Feature vector  OUTPUT : A/C mode 22 NTU CSIE iAgent Lab

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

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

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

Experiment Setting  Each zone builds a model 1.3-fold cross validation 2.The weather pattern in testing data doesn’t exist in training data Does not collect all the weather patterns 26 NTU CSIE iAgent Lab

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

Preprocessing  Missing data treatment Encoding Recognize the data is missing or not Linear interpolation The change of temperature or humidity is linear  Normalization Min-max normalization : [0,1] It prevents features with large scale biasing the result 28 NTU CSIE iAgent Lab

Experiment Result  Result The model achieves high accuracy The model can recognize the data with the weather pattern not included in training data 204_5 has the highest accuracy 29 NTU CSIE iAgent Lab ZoneExperiment 1Experiment 2 204_198.5%87.0% 204_289.1%85.0% 204_399.8%98.4% 204_497.9%90.2% 204_599.9%99.0% 204_693.9%86.3% 336_293.2%92.1%

Thermal Comfort Calculation 30 NTU CSIE iAgent Lab

Thermal Comfort Calculation  GOAL : Find the thermal comfort range to determine the indoor temperature being too cold or too hot  INPUT : Questionnaire  OUTPUT : Thermal comfort range 31 NTU CSIE iAgent Lab

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

Thermal Sensation Scale  Thermal sensation scale [ASHRAE Standard 55] Adaptive method to get PMV Thermal sensation vote (TSV) 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

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

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

Data Collection R204 (computer class room)R336 (laboratory) PeriodMarch July 2010December February 2011 Number1,7451, NTU CSIE iAgent Lab 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%)

Result 37 NTU CSIE iAgent Lab  Linear regression equation T C = T O 1.T C = T O (Worldwide) 2.T C = T O (Hong Kong) 3.T C = T O (Taiwan)

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

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

Thermal Comfort Range 40 NTU CSIE iAgent Lab 2.67

A/C State Evaluation 41 NTU CSIE iAgent Lab

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

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

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

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

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

Valid Data  From January 2011 to May 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% R %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%

Professor Room - R NTU CSIE iAgent Lab State (April 2011)NumberPercentageColor 0 : no people but A/C is turn on(abnormal)2,2016.9%Yellow 1 : too cold (abnormal)2420.8%Blue 2 : too hot (abnormal) 10%Red 3 : others (normal)29, %Green distribution during a week weekday weekend

Class Room – R NTU CSIE iAgent Lab distribution during a week State (April 2011)NumberPercentageColor 0 : no people but A/C is turn on(abnormal)6281.8%Yellow 1 : too cold (abnormal)3,0629.0%Blue 2 : too hot (abnormal) 10%Red 3 : others (normal)30, %Green weekday weekend

Computer Class Room – R NTU CSIE iAgent Lab State (April 2011)NumberPercentageColor 0 : no people but A/C is turn on(abnormal)5, %Yellow 1 : too cold (abnormal)18, %Blue 2 : too hot (abnormal) 140%Red 3 : others (normal)7, %Green weekday weekend

Class Room – R NTU CSIE iAgent Lab State (April 2011)NumberPercentageColor 0 : no people but A/C is turn on(abnormal)15, %Yellow 1 : too cold (abnormal)00.0%Blue 2 : too hot (abnormal) 5, %Red 3 : others (normal)19, %Green weekday weekend

Seminar Room 52 NTU CSIE iAgent Lab State (April 2011)R324R439R521 0 : no people but A/C is turn on(abnormal)3.6%8.6%5.9% 1 : too cold (abnormal)5.5%2.9%4.4% 2 : too hot (abnormal)0.0% 3 : others (normal)90.9%88.4%89.6% R324 R439 R521

Result  Professor room Cooling mode is too cold for the professor, so State 0 happens  Class room Administrator decreases a lot of abnormal states  Computer class room Students does not change the A/C mode even if State 1 happens  Laboratory State 0 happens often in midnight  Seminar room State 0 and State 1 takes about 10% 53 NTU CSIE iAgent Lab

R204 and R 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

Outline  Introduction  System Architecture  Analysis  Conclusion 55 NTU CSIE iAgent Lab

Conclusion and Contribution  Data collection : more than five months and continuously  A/C mode recognition model : accuracy is higher than 85%  Thermal comfort range : 19.32℃ and 24.67℃  Abnormal A/C states Professor room : 7.7% Class room : 10.8% Computer class room : 75.9% Laboratory : 52.4% Seminar room : 10.3% 56 NTU CSIE iAgent Lab

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 57 NTU CSIE iAgent Lab

Thank You Q & A 58 NTU CSIE iAgent Lab

2010/10/14 59 NTU CSIE iAgent Lab

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Abnormal A/C State in NTU CSIE  Ideal power consumption 62 NTU CSIE iAgent Lab KWH

Abnormal A/C State in NTU CSIE  Real power consumption 63 NTU CSIE iAgent Lab KWH A/C is turned off ?

Deployment 64 NTU CSIE iAgent Lab  One server per floor (1F to 5F)  Relays deploy around the corridors

標記 2010/12/09 65 NTU CSIE iAgent Lab 地點時間 時間長度 ( 分鐘 ) R10411/14 00:00 – 11/15 17: R11311/12 14:17 – 11/15 17: R20411/13 11:43 – 11/15 10: R31811/13 11:34 – 11/15 17: R32411/13 12:33 – 11/15 10: R33611/12 08:47 – 11/15 11: R43911/13 11:30 – 11/15 10: R51311/12 14:27 – 11/15 14: R52111/14 14:55 – 11/15 17:301595

2010/10/14 66 NTU CSIE iAgent Lab

67 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 {0,1} 樓層 {0,1} 房間類型 {0,1} 區域編號 {0,1} 建積 311float 星期幾 {0,1} 周間 / 周末 {0,1} 學期中 / 寒暑假 {0,1} 小時 {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

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, /12/01 68 NTU CSIE iAgent Lab

 perhaps bringing up Structural Risk Minimization versus traditional Empirical Risk Minimization as it relates to the avoidance of local minima and overfitting 69 NTU CSIE iAgent Lab

70 NTU CSIE iAgent Lab Zone(Experiment 1)SVM(linear)SVM(RBF) Additive Logistic Regression 204_197.6%98.5%98.4% 204_289.0%89.1%96.2% 204_399.8% 204_494.3%97.9%98.0% 204_599.9% 204_693.7%93.9%94.2% 336_293.2% 93.4%

Cross-Validation : 3-Fold 71 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_ _ _ _ _ _ _ Avg

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 PPD=100-95*e *PMV^ *PMV^2 73 NTU CSIE iAgent Lab

Thermal Comfort Range 74 NTU CSIE iAgent Lab

A/C State 75 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

R 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

Seminar Room – R 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

R 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