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

Outline

IntroductionSystem AnalysisConclusion

2NTU CSIE iAgent Lab

Smart A/C

Energy Saving


Reason

Policy

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Power Consumption in a Building


(source : Continental Automated Buildings Association, CABA)

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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

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Architecture of Central A/C System

Chilled water host• Evaporator• Condenser

Other devices• Pump• Cooling tower


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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

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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 )

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Abnormal A/C State in NTU CSIE

Ideal power consumption


KW

H

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Real power consumption


KW

H

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Hot Cold

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Outline



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System Overview


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Wireless Sensor Network


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Sensors

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


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Nodes in the Sensor Network


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

Relay Receiver

• Data saving : 1 minute

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Collection Unit


Room : divide into zones according to A/C controllerEnvironmental data

• temperature and humidity : vent, indoor• occupancy state

indoorventmotion sensor

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Deployment


One server per floor (1F to 5F)Relays deployed around the corridorsRoom

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

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A/C Mode Recognition


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A/C Mode Recognition

Goal : using machine learning to train the model for recognizing the A/C mode Input : environmental dataOutput : A/C mode ∈ {off , venting , cooling}Mode

• Off mode : blower= off• Venting mode : blower = on , valve = off• Cooling mode : blower= on , valve = on


Smart A/C

Dataset

Period PlaceNovember 2010 R104, R204, R318, R324, R336, R439, R521December 2010 - January 2011 R204, R324, R336February 2010 - March 2011 R104, R204


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%

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Feature Extraction


Temperature and humidity• Indoor• Vent• Outdoor

Delta (temperature and humidity)Parameters of the central A/C systemSpatial

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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


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Experiment Setting

Execution environment : wekaLearning algorithm : SVM

• Kernel function : RBFScenario1. 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

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Steps of the Experiment 2


outdoor temperatureoutdoor

humidity

Clustering the dataset• Algorithm : k-means (k=4)• Feature: outdoor temperature, outdoor humidity• Color : outdoor weather pattern

4-fold cross validation

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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)


Zone Experiment 1 Experiment 2204_1 98.6% 87.0%204_2 89.1% 85.0%204_3 99.8% 98.4%204_4 98.0% 90.2%204_5 99.9% 99.0%204_6 93.9% 86.3%336_2 93.3% 92.1%

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Thermal Comfort Calculation


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Thermal Comfort Calculation

GOAL :• Find thermal comfort range of the environment

INPUT : • Questionnaire

OUTPUT :• Thermal comfort range


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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


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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


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

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Thermal Comfort - Linear Regression

Field survey• Collect thermal sensation vote (TSV) • Outdoor temperature has the highest relevance with thermal comfort 1. 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

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}


VALID !

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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%)

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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)

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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)


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Thermal Comfort Range


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

2.67

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2.67

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A/C State Evaluation


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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 rangeOUTPUT :

• A/C state40NTU CSIE iAgent Lab

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

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Outline



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Analysis of Abnormal A/C States


Abnormal A/C States Detecting System normal/abnormal Analysis User

useful information

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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) Percentage0 : 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 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

Class Room – R104


State (April 2011) Percentage0 : 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%)


Smart A/C

Computer Class Room – R204


State (April 2011) Percentage0 : 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%)


Smart A/C

Class Room – R336


State (April 2011) Percentage0 : 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%)


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

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Outline



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Conclusion and Contribution

Collect the environmental data in NTU CSIE for more than five monthsBuild a SVM model to recognize the A/C mode for each zoneCalculate the regression line and the range of the thermal comfort in NTU CSIE from questionnairesThe proposed system is useful in detecting abnormal A/C states and providing analysis to modify user behaviors


Smart A/C

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 usersRecognizing the activity level for reducing the power consumption of A/C


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Thank You

Q & A


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2010/10/14 55NTU CSIE iAgent Lab

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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

analysis 2


用電量

高溫下用電群

中溫下用電群

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

同上

Smart A/C

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

Smart A/C

將每小時歸為某種天氣型態 ( 三群 )

低溫中溫高溫OutdoorAVG 14.0 (8.0 – 16.9) 19.8(16.9 – 22.8) 25.8(22.8 – 34.1)

# of each clustering 1621(45%) 1286(35%) 717(20%)


Smart A/C

每種天氣型態之用電量分群 ( 三群 )


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)


中溫 Label = 低用電 Label = 中用電 Label = 高用電Power Consumption 134.9(128.8 – 138.4) 142.0(138.5 – 146.9) 152.7(147.4 – 169.3)


高溫 Label = 低用電 Label = 中用電 Label = 高用電Power Consumption 145.8 (133.3 – 150.8) 156.3 (151.1 – 163.1) 170.8 (164.0 – 187.4)


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)


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