Presenter : Min- Chia Chang Advisor : Prof. Jane Hsu Date : 201 1 - 06 -30

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 NetworkPresenter : Min-Chia ChangAdvisor : Prof. Jane HsuDate : 2011-06 -30

Smart A/C

OutlineIntroductionSystem Architecture AnalysisConclusion

2NTU CSIE iAgent Lab

Smart A/C

Energy Saving


Reason

Policy

Smart A/C

Power Consumption in a Building

4NTU CSIE iAgent Lab(source : Continental Automated Buildings Association, CABA)

Smart A/C

Architecture of Central A/C SystemChilled water host• Evaporator• Condenser

Other devices• Pump• Cooling tower


Smart A/C

Energy Conservation for Central A/C System


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 )

Smart A/C

Power Consumption in NTU CSIETotal • 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 - 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

Smart A/C

Control of Central A/C System


Central• Chilled water host• Off mode• On mode (All year on duty)

Local• A/C controller• Off mode• Venting mode• Cooling mode

Smart A/C

Ideal A/C power consumption• Assumption : people number ∝ A/C power consumption

Abnormal A/C State in NTU CSIE


KWH

Smart A/C

Abnormal A/C State in NTU CSIEReal A/C power consumption• From electricity meter


KWH

A/C is turned off ? 20KWH

Smart A/C

Abnormal A/C State in NTU CSIE


Hot Cold

Smart A/C



Smart A/C

System Overview


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Wireless Sensor Network


Smart A/C

SensorsPlatform : Taroko• Temperature and humidity sensor : SHT11• Infrared motion sensor


Smart A/C

Nodes in the Sensor Network


Sender• (temperature, humidity, ID)• (preamble, motion value, ID)

Receiver• Data saving : 1 minute

Relay

Smart A/C

Deploy Unit


Room : divide into zones according to A/C controllerEnvironmental data• temperature and humidity• vent• indoor•motion value

indoorventmotion sensor

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Deployment


One server per floor (1F to 5F)Relays deploy around the corridors

Smart A/C

DeploymentRoom• Class room : R104• Computer class room : R204• Professor room : R318• Laboratory : R336• Seminar room : R324, R439, R521


Smart A/C

A/C Mode Recognition


Smart A/C

A/C ModeMode• Off mode : blower= off , valve = off• Venting mode : blower = on , valve = off• Cooling mode : blower= on , valve = on


wind velocity A/Cmode temperaturesetting

A/C power

OffVentingCooling indoor temperature≧＜

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A/C Mode RecognitionGOAL : • Using machine learning to build the model for recognizing the A/C mode

INPUT :• Feature vector

OUTPUT : • A/C mode


Smart A/C

Features


Category Feature Dimension TypeTemperatureand Humidity TI , HI , TV , HV , TO , HO 6 Float

Delta ΔTI,V , ΔHI,V ΔTI,O , ΔHI,O ΔTO,V , ΔHO,V 6 FloatParameters (Central A/C ) Host 3 {0,1}Leaving Temperature 1 Float

Rotation Speed of Pump 1 FloatSpatial Building 2 {0,1}Floor 5 {0,1}Room Type 5 {0,1}Area 1 Float

Smart A/C

Annotation

Method 1• Control on purpose

Method 2• Record by camera• Temporal feature


Period Place AnnotationNovember 2010 R104, R204, R318, R324, R336, R439, R521 Method1December 2010 - January 2011 R204, R324, R336 Method2February 2010 - March 2011 R104, R204 Method2

Smart A/C

Dataset


Place Total Data Label =off Label = venting Label = cooling Missing Data204_1 17,345 6,371 6,817 4,157 4,686 27.0%204_2 17,106 6,569 8,168 2,369 4,603 26.9%204_3 16,694 6,357 5,381 4,956 4,576 27.4%204_4 16,415 8,592 5,014 2,809 4,632 28.2%204_5 17,487 6,569 0 10,918 6,054 34.6%204_6 15,616 6,794 5,843 2,979 9,158 58.7%336_2 20,889 6,439 10,100 4,350 5,931 28.4%

Smart A/C

Experiment SettingEach zone builds a model1. 3-fold cross validation2. The weather pattern in testing data doesn’t exist in training data• Does not collect all the weather patterns


Smart A/C

Steps of the Experiment 2

27NTU CSIE iAgent Laboutdoor temperatureoutd

oor hum

idity

Cluster • Algorithm : k-means (k=4)• Feature: outdoor temperature, outdoor humidity

Leave-one-out Cross Validation

336_2

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PreprocessingMissing 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


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Experiment ResultResult• 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


Zone Experiment 1 Experiment 2204_1 98.5% 87.0%204_2 89.1% 85.0%204_3 99.8% 98.4%204_4 97.9% 90.2%204_5 99.9% 99.0%204_6 93.9% 86.3%336_2 93.2% 92.1%

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Thermal Comfort Calculation


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Thermal Comfort CalculationGOAL :• Find the thermal comfort range to determine the indoor temperature being too cold or too hot

INPUT : • Questionnaire

OUTPUT :• Thermal comfort range


Smart A/C

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


Smart A/C

Thermal Sensation ScaleThermal 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


Scale Thermal sensation+3 Hot+2 Warm+1 Slightly warm0 Neutral-1 Slightly cool-2 Cool-3 Cold

Smart A/C

Thermal Comfort - Linear RegressionField survey• Collect thermal sensation vote• Outdoor temperature has the highest relevance1. TC = 17.8 + 0.31TO (Worldwide) [deDear and Brager 1998]2. TC = 18.3 + 0.158TO (Hong Kong) [Mui and Chan 2003]3. TC = 15.5 + 0.29TO (Taiwan) [Lin et al. 2008]


Smart A/C

QuestionnaireThermal 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}


VALID !

Smart A/C

Data Collection

R204 (computer class room) R336 (laboratory)Period March 2010 - July 2010 December 2010 - February 2011Number 1,745 1,033


-3 -2 -1 0 +1 +2 +3Comfortable 55(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%)

Smart A/C

Result


Linear regression equation• TC = 20.6+ 0.107TO

1. TC = 17.8 + 0.31TO (Worldwide)2. TC = 18.3 + 0.158TO (Hong Kong)3. TC = 15.5 + 0.29TO (Taiwan)

Smart A/C

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)


Smart A/C

Thermal Comfort Range


Regression• Indoor temperature • Mean thermal sensation vote (PMV) during each ℃

2.67

Smart A/C



2.67

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A/C State Evaluation


Smart A/C

A/C State EvaluationGOAL :• 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


Smart A/C

A/C State


people in the room

A/C = turned onY N

Y Nnormalabnormal

A/C = cooling modeY N

normalindoor temperature? comfort rangelower higherwithin

normalabnormal abnormal

Smart A/C



Smart A/C

Analysis of Abnormal A/C States


Abnormal A/C States Detecting System normal/abnormal history data analysis User

useful information

Smart A/C

Target RoomRoom• Class room : R104• Computer class room : R204• Professor room : R318• Laboratory : R336• Seminar room : R324, R439, R521


Smart A/C

Valid DataFrom January 2011 to May 2011


Place January February March April MayR104 36,525 82% 33,045 82% 36,958 83% 34,076 79% 6,072 14%R204 39,135 88% 15,401 38% 28,123 63% 31,167 72% 13,958 31%R318 33,444 75% 32,053 79% 35,742 80% 31,978 74% 34,806 78%R324 30.993 69% 26,722 66% 29,890 67% 24,978 58% 29,212 65%R336 35,277 79% 28,872 72% 43,088 97% 39,920 92% 40,604 91%R439 39,681 89% 24,284 60% 35,212 79% 30,658 71% 34,158 77%R521 386,59 87% 34,927 87% 38,171 86% 26,992 62% 18,657 42%

Smart A/C

Professor Room - R318


State (April 2011) Number Percentage Color0 : no people but A/C is turn on(abnormal) 2,201 6.9% Yellow1 : too cold (abnormal) 242 0.8% Blue2 : too hot (abnormal) 1 0% Red3 : others (normal) 29,534 92.4% Greendistribution during a weekweekday weekend

Smart A/C

Class Room – R104


distribution during a week

State (April 2011) Number Percentage Color0 : no people but A/C is turn on(abnormal) 628 1.8% Yellow1 : too cold (abnormal) 3,062 9.0% Blue2 : too hot (abnormal) 1 0% Red3 : others (normal) 30,386 89.2% Greenweekday weekend

Smart A/C

Computer Class Room – R204


State (April 2011) Number Percentage Color0 : no people but A/C is turn on(abnormal) 5,582 17.9% Yellow1 : too cold (abnormal) 18,080 58.0% Blue2 : too hot (abnormal) 14 0% Red3 : others (normal) 7,491 24.0% Greenweekday weekend

Smart A/C

Class Room – R336


State (April 2011) Number Percentage Color0 : no people but A/C is turn on(abnormal) 15,559 39.0% Yellow1 : too cold (abnormal) 0 0.0% Blue2 : too hot (abnormal) 5,360 13.4% Red3 : others (normal) 19,001 47.6% Green

weekday weekend

Smart A/C

Seminar Room


State (April 2011) R324 R439 R5210 : 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% 0.0% 0.0%3 : others (normal) 90.9% 88.4% 89.6%

R324 R439 R521

Smart A/C

ResultProfessor 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%


Smart A/C

R204 and R336


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

Smart A/C

Outline IntroductionSystem Architecture AnalysisConclusion


Smart A/C

Conclusion and ContributionData collection : more than five months and continuouslyA/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%


Smart A/C

Future Work Improve the quality of the wireless sensor network Use persuasive technology to provide the results for usersRecognize the activity level of NTU CSIE in each time interval


Smart A/C

Thank You

Q & A


Smart A/C

2010/10/14 59NTU CSIE iAgent Lab

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Smart A/C


時間入出 ( 06:00 )累積人數從開始01:00 X X 1902:00 7 16 1003:00 3 4 904:00 1 5 505:00 1 6 006:00 3 1 207:00 10 2 408:00 59 16 4709:00 90 24 11310:00 86 50 14911:00 77 84 14212:00 155 190 10713:00 173 88 19214:00 144 69 26715:00 101 133 23516:00 33 41 22717:00 67 204 9018:00 175 167 9819:00 85 71 11220:00 24 40 9621:00 24 88 32

Smart A/C

Abnormal A/C State in NTU CSIEIdeal power consumption


KWH

Smart A/C

Abnormal A/C State in NTU CSIEReal power consumption


KWH

A/C is turned off ?

Smart A/C

Deployment


One server per floor (1F to 5F)Relays deploy around the corridors

Smart A/C

標記


地點時間時間長度 ( 分鐘 )R104 11/14 00:00 – 11/15 17:27 2487R113 11/12 14:17 – 11/15 17:21 4504R204 11/13 11:43 – 11/15 10:35 2812R318 11/13 11:34 – 11/15 17:22 3228R324 11/13 12:33 – 11/15 10:34 2761R336 11/12 08:47 – 11/15 11:45 4498R439 11/13 11:30 – 11/15 10:33 2823R513 11/12 14:27 – 11/15 14:23 4316R521 11/14 14:55 – 11/15 17:30 1595

Smart A/C


Smart A/C


context data index dimensions Value室內溫 / 濕度 (raw) 1-2 2 Float出風口溫 / 濕度 (raw) 3-4 2 Float室外溫 / 濕度 5-6 2 Float冰水主機 7-9 3 {0,1}出水溫度 10 1 Integer泵浦轉速 11 1 Float舊館 / 新館 12-13 2 {0,1}樓層 14-18 5 {0,1}房間類型 19-24 6 {0,1}區域編號 25-30 6 {0,1}建積 31 1 float星期幾 32-38 7 {0,1}周間 / 周末 39-40 2 {0,1}學期中 / 寒暑假 41-42 2 {0,1}小時 43-66 24 {0,1}室內溫 / 濕度 (Interpolation) 67-68 2 Float出風口溫 / 濕度 (Interpolation) 69-70 2 Float室內溫 / 濕度 (Encode) 71-72 2 Float出風口溫 / 濕度 (Encode) 73-74 2 Float差距 ( 室內 , 出風口 ) 75-76 2 Float差距 ( 室內 , 室外 ) 77-78 2 Float差距 ( 出風口 , 室外 ) 79-80 2 Float

Smart A/C

Dataset D={(xn,yn)}, 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


Smart A/C

perhaps bringing up Structural Risk Minimization versus traditional Empirical Risk Minimization as it relates to the avoidance of local minima and overfitting


Smart A/C


Zone(Experiment 1) SVM(linear) SVM(RBF) Additive Logistic Regression204_1 97.6% 98.5% 98.4%204_2 89.0% 89.1% 96.2%204_3 99.8% 99.8% 99.8%204_4 94.3% 97.9% 98.0%204_5 99.9% 99.9% 99.9%204_6 93.7% 93.9% 94.2%336_2 93.2% 93.2% 93.4%

Smart A/C

Cross-Validation : 3-Fold


T(V) TH(V) TH(V), TH(I) TH(V), TH(I),TH(O)

TH(V), TH(I), TH(O), AChost , ACdegree , ACspeed

204_1 0.68 0.80 0.84 0.96 0.98

204_2 0.70 0.85 0.86 0.97 0.99

204_3 0.68 0.82 0.83 0.97 0.97

204_4 0.71 0.84 0.88 0.97 0.98

204_5 0.87 0.87 0.88 0.97 0.99

204_6 0.60 0.63 0.81 0.96 0.98

336_2 0.87 0.87 0.87 0.96 0.98

Avg. 0.77 0.81 0.85 0.97 0.98

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Smart A/C

PPD=100-95*e-0.03353*PMV^4-0.2179*PMV^2


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A/C State


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

Smart A/C

R324


Event R104_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 weekenddistribution during a week

Smart A/C

Seminar Room – R439


State (April 2011) Percentage0 : 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%)


Smart A/C

R521


Event R104_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)


Presenter : Min- Chia Chang Advisor : Prof. Jane Hsu Date : 201 1 - 06 -30

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Transcript of Presenter : Min- Chia Chang Advisor : Prof. Jane Hsu Date : 201 1 - 06 -30