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
Outline
 Introduction
 System
 Analysis
 Conclusion
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Energy Saving
 Reason
 Policy
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Power Consumption in a Building
(source : Continental Automated Buildings Association, CABA)
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Architecture of Central A/C System
 Chilled water host
• Evaporator
• Condenser
 Other devices
• Pump
• Cooling tower
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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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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
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Abnormal A/C State in NTU CSIE
Ideal power consumption
KWH
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Abnormal A/C State in NTU CSIE
Real power consumption
A/C is turned off ?
KWH
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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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Abnormal A/C State in NTU CSIE
Hot
Cold
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Outline
 Introduction
 System
 Analysis
 Conclusion
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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)
 Receiver
• Data saving : 1 minute
 Relay
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Collection Unit
 Room : divide into zones according to A/C controller
 Environmental data
• temperature and humidity
• vent
• indoor
• occupancy state
vent
indoor
motion sensor
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Deployment
 One server per floor (1F to 5F)
 Relays deploy around the corridors
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Deployment
 Room
• Class room : R104
• Computer class room : R204
• Professor room : R318
• Laboratory : R336
• Seminar room :
R324, R439, R521
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A/C Mode Recognition
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A/C Mode
 Mode
• Off mode
: blower= off
• Venting mode : blower = on , valve = off
• Cooling mode : blower= on , valve = on
A/C
wind
velocity
A/C
Off
Venting
Cooling
mode
power
temperature
setting
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≧
＜
indoor
temperature
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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
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Features
Category
Feature
Dimension
Type
Temperature
and Humidity
TI , HI , TV , HV , TO , HO
6
Float
ΔTI,V , ΔHI,V
ΔTI,O , ΔHI,O
ΔTO,V , ΔHO,V
6
Float
Host
3
{0,1}
Leaving Temperature
1
Float
Rotation Speed of Pump
1
Float
Building
2
{0,1}
Floor
5
{0,1}
Room Type
5
{0,1}
Area
1
Float
Delta
Parameters
(Central A/C )
Spatial
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Annotation
Period
Place
Annotation
November 2010
R104, R204, R318, R324, R336, R439, R521
Method1
December 2010 January 2011
R204, R324, R336
Method2
February 2010 March 2011
R104, R204
Method2
 Method 1
• Control on purpose
 Method 2
• Record by camera
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Dataset
Place
Total Data
Label =
off
Label =
venting
Label =
cooling
Missing Data
204_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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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
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Steps of the Experiment 2
 Cluster
• Algorithm : k-means (k=4)
• Feature: outdoor temperature, outdoor humidity
 Leave-one-out Cross Validation
outdoor humidity
336_2
outdoor temperature
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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
• 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
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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 2
204_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
Scale
Thermal
sensation
+3
Hot
+2
Warm
+1
Slightly warm
0
Neutral
-1
Slightly cool
• -1, 0, +1 : yes
-2
Cool
• -2, -3, +2, +3 : no
-3
Cold
• Constraints
• Metabolic rate : 1.0Met - 2.0Met
• Clothing insulation : ≦ 1.5 Clo
• Comfortable or not
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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]
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Questionnaire
 Thermal sensation scale : {-3, -2, -1, 0 ,+1, +2, +3}
 Direct question : {comfortable, not comfortable}
 Metabolic rate : {after sport, static activity}
VALID !
 Clothing insulation : {sleeveless, shirt-sleeve, long-sleeve, thick coat}
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Data Collection
R204 (computer class room)
R336 (laboratory)
Period
March 2010 - July 2010
December 2010 - February 2011
Number
1,745
1,033
-3
-2
-1
0
+1
+2
+3
Comfortable
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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Thermal Comfort Range
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 range
 OUTPUT :
• A/C state
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A/C State
people in the room
Y
A/C = cooling mode
N
A/C = turned on
N
Y
indoor temperature
? comfort range
lower
within
higher
abnormal
normal
abnormal
normal
Y
abnormal
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N
normal
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Outline
 Introduction
 System
 Analysis
 Conclusion
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Analysis of Abnormal A/C States
Abnormal A/C States
Detecting System
normal/
abnormal
history
data
analysis
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useful
information
User
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Target Room
 Room
• Class room : R104
• Computer class room : R204
• Professor room : R318
• Laboratory : R336
• Seminar room : R324, R439, R521
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Valid Data
 From January 2011 to May 2011
Place
January
February
March
April
May
R104
36,525 82% 33,045 82% 36,958 83% 34,076 79%
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%
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6,072 14%
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Professor Room - R318
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)
distribution during a week
29,534 (92.4% )
weekday
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weekend
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Seminar Room – R439
(April 2011) Percentage
State
2,650 (8.6%)
0 : no people but AC not closed (abnormal)
897 (2.9%)
1 : too cold (abnormal)
0 (0.0%)
2 : too hot (abnormal)
27,111 (88.4%)
3 : others (normal)
distribution during a week
weekday
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weekend
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Class Room – R104
(April 2011) Percentage
State
628 (1.8%)
0 : no people but AC not closed (abnormal)
3,062 (9.0%)
1 : too cold (abnormal)
1 (0%)
2 : too hot (abnormal)
30,386 (89.2%)
3 : others (normal)
distribution during a week
weekday
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weekend
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Computer Class Room – R204
(April 2011) Percentage
State
5,582 (17.9%)
0 : no people but AC not closed (abnormal)
18,080 (58.0%)
1 : too cold (abnormal)
14 (0%)
2 : too hot (abnormal)
7,491 (24.0%)
3 : others (normal)
distribution during a week
weekday
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weekend
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Class Room – R336
(April 2011) Percentage
State
15,559 (39.0%)
0 : no people but AC not closed (abnormal)
0 (0.0%)
1 : too cold (abnormal)
5,360 (13.4%)
2 : too hot (abnormal)
19,001 (47.6%)
3 : others (normal)
distribution during a week
weekday
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weekend
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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
 Introduction
 System
 Analysis
 Conclusion
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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
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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
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Thank You
Q&A
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2010/10/14
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標記
地點
時間
時間長度(分鐘)
R104
11/14 00:00 – 11/15 17:27
2487
R113
11/12 14:17 – 11/15 17:21
4504
R204
11/13 11:43 – 11/15 10:35
2812
R318
11/13 11:34 – 11/15 17:22
3228
R324
11/13 12:33 – 11/15 10:34
2761
R336
11/12 08:47 – 11/15 11:45
4498
R439
11/13 11:30 – 11/15 10:33
2823
R513
11/12 14:27 – 11/15 14:23
4316
R521
11/14 14:55 – 11/15 17:30
1595
2010/12/09
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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
2010/12/01
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2010/10/14
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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
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iAgent Lab
Float
62
Cross-Validation : 3-Fold
T(V)
TH(V)
TH(V), TH(I) TH(V), TH(I), TH(V), TH(I), TH(O),
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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Thermal Comfort Range
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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
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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
weekend
distribution during a week
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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)
weekday
weekend
distribution during a week
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