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 Architecture
Analysis
Conclusion
NTU CSIE iAgent Lab
2
Energy Saving
 Reason
 Policy
NTU CSIE iAgent Lab
3
Power Consumption in a Building
(source : Continental Automated Buildings Association, CABA)
NTU CSIE iAgent Lab
4
Architecture of Central A/C System
Chilled water host
• Evaporator
• Condenser
Other devices
• Pump
• Cooling tower
NTU CSIE iAgent Lab
5
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)
NTU CSIE iAgent Lab
6
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 - May 2011 )
• 3,693.8 KHW/day
• 40.88% of the total
7
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
NTU CSIE iAgent Lab
8
Abnormal A/C State in NTU CSIE
Ideal A/C power consumption
• Assumption : people number ∝ A/C power consumption
KWH
NTU CSIE iAgent Lab
9
Abnormal A/C State in NTU CSIE
Real A/C power consumption
• From electricity meter
20KWH
A/C is turned off ?
KWH
NTU CSIE iAgent Lab
10
Abnormal A/C State in NTU CSIE
Hot
Cold
NTU CSIE iAgent Lab
11
Outline
Introduction
System Architecture
Analysis
Conclusion
NTU CSIE iAgent Lab
12
System Overview
NTU CSIE iAgent Lab
13
Wireless Sensor Network
NTU CSIE iAgent Lab
14
Sensors
Platform : Taroko
• Temperature and humidity sensor : SHT11
• Infrared motion sensor
NTU CSIE iAgent Lab
15
Nodes in the Sensor Network
 Sender
• (temperature, humidity, ID)
• (preamble, motion value, ID)
 Receiver
• Data saving : 1 minute
 Relay
NTU CSIE iAgent Lab
16
Deploy Unit
Room : divide into zones according to A/C controller
Environmental data
• temperature and humidity
• vent
• indoor
• motion value
vent
indoor
motion sensor
NTU CSIE iAgent Lab
17
Deployment
 One server per floor (1F to 5F)
 Relays deploy around the corridors
NTU CSIE iAgent Lab
18
Deployment
Room
• Class room : R104
• Computer class room : R204
• Professor room : R318
• Laboratory : R336
• Seminar room :
R324, R439, R521
NTU CSIE iAgent Lab
19
A/C Mode Recognition
NTU CSIE iAgent Lab
20
A/C Mode
 Mode
• Off mode
: blower= off , valve = 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
NTU CSIE iAgent Lab
indoor
≧ temperature
＜
21
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
NTU CSIE iAgent Lab
22
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
NTU CSIE iAgent Lab
23
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
• Temporal feature
NTU CSIE iAgent Lab
24
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%
NTU CSIE iAgent Lab
25
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
NTU CSIE iAgent Lab
26
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
NTU CSIE iAgent Lab
27
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
NTU CSIE iAgent Lab
28
Experiment Result
Result
• The model achieves high accuracy
• The model can recognize the data with the weather pattern
not included in training data
Zone
Experiment 1 Experiment 2
• 204_5 has the highest accuracy
204_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%
NTU CSIE iAgent Lab
29
Thermal Comfort Calculation
NTU CSIE iAgent Lab
30
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
NTU CSIE iAgent Lab
31
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
NTU CSIE iAgent Lab
32
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
Scale
Thermal
sensation
+3
Hot
+2
Warm
+1
Slightly warm
0
Neutral
-1
Slightly cool
-2
Cool
-3
Cold
• -2, -3, +2, +3 : no
NTU CSIE iAgent Lab
33
Thermal Comfort - Linear Regression
Field survey
• Collect thermal sensation vote
• Outdoor temperature has the highest relevance
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]
NTU CSIE iAgent Lab
34
Questionnaire
 Thermal sensation scale : {-3, -2, -1, 0 ,+1, +2, +3}
 Direct question : {comfortable, not comfortable}
 Metabolic rate : {after sport, static activity}
VALID !
 Insulation : {sleeveless, shirt-sleeve, long-sleeve, thick coat}
NTU CSIE iAgent Lab
35
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%)
NTU CSIE iAgent Lab
36
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)
NTU CSIE iAgent Lab
37
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)
NTU CSIE iAgent Lab
38
Thermal Comfort Range
 Regression
• Indoor temperature
• Mean thermal sensation vote (PMV) during each ℃
2.67
NTU CSIE iAgent Lab
39
Thermal Comfort Range
2.67
NTU CSIE iAgent Lab
40
A/C State Evaluation
NTU CSIE iAgent Lab
41
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
NTU CSIE iAgent Lab
42
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
NTU CSIE iAgent Lab
N
normal
43
Outline
Introduction
System Architecture
Analysis
Conclusion
NTU CSIE iAgent Lab
44
Analysis of Abnormal A/C States
Abnormal A/C States
Detecting System
normal/
abnormal
history
data
analysis
NTU CSIE iAgent Lab
useful
information
User
45
Target Room
 Room
• Class room : R104
• Computer class room : R204
• Professor room : R318
• Laboratory : R336
• Seminar room : R324, R439, R521
NTU CSIE iAgent Lab
46
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%
NTU CSIE iAgent Lab
6,072 14%
47
Professor Room - R318
State (April 2011)
0 : no people but A/C is turn on(abnormal)
1 : too cold (abnormal)
Number
Percentage
Color
2,201
6.9%
Yellow
242
0.8%
Blue
1
0%
Red
29,534
92.4%
Green
2 : too hot (abnormal)
3 : others (normal)
weekday
distribution during a week
NTU CSIE iAgent Lab
weekend
48
Class Room – R104
State (April 2011)
Number
Percentage
Color
628
1.8%
Yellow
3,062
9.0%
Blue
1
0%
Red
30,386
89.2%
Green
0 : no people but A/C is turn on(abnormal)
1 : too cold (abnormal)
2 : too hot (abnormal)
3 : others (normal)
weekday
distribution during a week
NTU CSIE iAgent Lab
weekend
49
Computer Class Room – R204
State (April 2011)
0 : no people but A/C is turn on(abnormal)
1 : too cold (abnormal)
Number
Percentage
Color
5,582
17.9%
Yellow
18,080
58.0%
Blue
14
0%
Red
7,491
24.0%
Green
2 : too hot (abnormal)
3 : others (normal)
weekend
weekday
NTU CSIE iAgent Lab
50
Class Room – R336
State (April 2011)
0 : no people but A/C is turn on(abnormal)
Number
Percentage
Color
15,559
39.0%
Yellow
0
0.0%
Blue
5,360
13.4%
Red
19,001
47.6%
Green
1 : too cold (abnormal)
2 : too hot (abnormal)
3 : others (normal)
weekend
weekday
NTU CSIE iAgent Lab
51
Seminar Room
State (April 2011)
R324
R439
R521
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%
0.0%
0.0%
90.9%
88.4%
89.6%
3 : others (normal)
R324
R439
NTU CSIE iAgent Lab
R521
52
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%
NTU CSIE iAgent Lab
53
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
NTU CSIE iAgent Lab
54
Outline
 Introduction
 System Architecture
 Analysis
 Conclusion
NTU CSIE iAgent Lab
55
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%
NTU CSIE iAgent Lab
56
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
NTU CSIE iAgent Lab
57
Thank You
Q&A
NTU CSIE iAgent Lab
58
2010/10/14
NTU CSIE iAgent Lab
59
NTU CSIE iAgent Lab
60
時間
入
01:00 X
02:00
03:00
04:00
05:00
06:00
07:00
08:00
09:00
10:00
11:00
12:00
13:00
14:00
15:00
16:00
17:00
18:00
19:00
20:00
21:00
出
X
7
3
1
1
3
10
59
90
86
77
155
173
144
101
33
67
175
85
24
24
16
4
5
6
1
2
16
24
50
84
190
88
69
133
41
204
167
71
40
88
累積人數(從06:00開始)
19
10
9
5
0
2
4
47
113
149
142
107
192
267
235
227
90
98
112
96
32
NTU CSIE iAgent Lab
61
Abnormal A/C State in NTU CSIE
Ideal power consumption
KWH
NTU CSIE iAgent Lab
62
Abnormal A/C State in NTU CSIE
Real power consumption
A/C is turned off ?
KWH
NTU CSIE iAgent Lab
63
Deployment
 One server per floor (1F to 5F)
 Relays deploy around the corridors
NTU CSIE iAgent Lab
64
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
NTU CSIE iAgent Lab
65
2010/10/14
NTU CSIE iAgent Lab
66
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
NTU CSIE
iAgent Lab
Float
67
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
NTU CSIE iAgent Lab
68
perhaps bringing up Structural Risk Minimization
versus traditional Empirical Risk Minimization as it
relates to the avoidance of local minima and
overfitting
NTU CSIE iAgent Lab
69
Zone(Experiment 1)
SVM(linear)
SVM(RBF)
Additive Logistic
Regression
204_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%
NTU CSIE iAgent Lab
70
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
NTU CSIE iAgent Lab
71
NTU CSIE iAgent Lab
72
PPD=100-95*e-0.03353*PMV^4-0.2179*PMV^2
NTU CSIE iAgent Lab
73
Thermal Comfort Range
NTU CSIE iAgent Lab
74
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
NTU CSIE iAgent Lab
75
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
NTU CSIE iAgent Lab
76
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
NTU CSIE iAgent Lab
weekend
77
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
NTU CSIE iAgent Lab
78