progress report presenter : min-chia chang advisor : prof. jane hsu date : 2011 - 03 - 01
TRANSCRIPT
OutlinePrediction of AC State (revised)Definition of AC Waste AnalysisResult of AC Waste AnalysisThesis – Chapter 2
2011/03/01 2NTU CSIE iAgent Lab
OutlinePrediction of AC State (revised)Definition of AC Waste AnalysisResult of AC Waste AnalysisThesis – Chapter 2
2011/03/01 3NTU CSIE iAgent Lab
Data- label: (OFF, ONno green, ONgreen) define⟶ y={0,1,2}
- OFF : close- ONno green : Tindoor < TuserSetting , valve = OFF- ONgreen : Tindoor > TuserSetting , valve = ON
- feature : define⟶ x , which is a vector- (Tindoor, Hindoor, Tvent, Hvent, Toutdoor, Houtdoor)- Context data
2011/03/01 4NTU CSIE iAgent Lab
Place- R104_1, R104_2, R104_3, R104_4 (classroom) - R204_1, R204_2, R204_3, R204_4, R204_5, R204_6 (computer classroom)- R318_1(professor room)- R324_1, R324_2 (seminar room)- R336_1, R336_2 (lab)- R439_1 (seminar room)- R521_1, R521_2 (seminar room)
2011/03/01 5NTU CSIE iAgent Lab
zone
note: each zone contains only one AC controller
Dataset D={( x n,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 6NTU CSIE iAgent Lab
DatasetTime- R336 : 2010-12-18 ~ 2011-01-06- R204 : 2011-01-06 ~ 2011-01-17- R324 : 2011-01-20 ~ 2011-01-30
2011/03/01 7NTU CSIE iAgent Lab
Execution environment Weka Function: SVM
- Kernel: RBF Cross Validation: 3-fold
- In each iteration :
2011/03/01 8NTU CSIE iAgent Lab
DatasetTrainingDataTestingData note : NEVER use testing data before
you predict.
AccuracyBaseline- x =(Tvent)
- accuracy = 72.66%- time =
- x =(Tindoor , Hindoor , Tvent , Hvent , Toutdoor , Houtdoor)- accuracy = 93.21% - time =
- x =(Tindoor , Hindoor , Tvent , Hvent , Toutdoor , Houtdoor , ……)
2011/03/01 9NTU CSIE iAgent Lab
Bagging (bootstrap aggregation)
2011/03/01 10NTU CSIE iAgent Lab
DatasetTrainingDataTestingData
K=?, S=?• K fixed - If S decreases, then time decreases.
• S fixed - If K increases, then the result of the vote is more convinced.
……
K training datasize = Ssize = S size = S
re-sampling
Bagging
2011/03/01 11NTU CSIE iAgent Lab
……
K training datasize = Ssize = S size = S
TrainingData re-samplingy=0 y=1 y=2
S/3 S/3 S/3
• K fixed - If S decreases, then time decreases.
K=?, S=?
2011/03/01 12NTU CSIE iAgent Lab
x=6dimensions K=1 K=10 K=30 K=50
S=300 63.14%16s
74.78%2m4s
80.4%6m10s
79.69%10m18s
S=1500 85.37%30s
87.90%4m20s
88.96%13m16s
89.20%22m8s
S=3000 88.27%47s
90.48%7m7s
91.03%21m36s
91.08%38m07s
S=15000 91.93%6m29s
92.52%31m11s
S=30000 92.55%19m18s
baseline:93.21%45m12s
time && accuracy => trade-off
Generate featurex=(Tindoor , Hindoor , Tvent , Hvent , Toutdoor , Houtdoor , some context data)
2011/03/01 13NTU CSIE iAgent Lab
context data dimensions valuechilled water host 3 {0,1}chilled water temperature 1 integerrotation speed of pump 1 floatnew or old (building) 2 {0,1}floor 5 {0,1}room type 6 {0,1}area 1 floatday of the week 7 {0,1}weekday or weekend 2 {0,1}semester or vacation 2 {0,1}hour of the day 24 {0,1}
2011/03/01 14NTU CSIE iAgent Lab
x=60dimensions
K=1 K=5 K=10 K=30 K=50
S=300 58.32%18s
78.11%1m23s
76.03%2m41s
79.38%7m31s
83.26%13m12s
S=1500 84.82%39s
89.12%3m12s
88.84%6m18s
91.27%18m12s
91.46%31m42s
S=3000 90.41%1m34s
92.33%7m23s
93.21%13m18s
93.60%40m14s
93.59%1h03m50s
S=15000 94.72%14m30s
95.38%1h12m55s
S=30000 95.43%39m40s
baseline:96.11%1h32m50s
K=?, S=?
Process the missing value missing value : Tindoor , Hindoor , Tvent , Hvent processing method :
method 1 : encoding e.g. : (?, ?, 15.2, ?) => (0, ?, 0, ?, 1, 15.2, 0, ?)
method 2 : interpolation (linear) e.g. : 2011-01-31 23:50 : (20, 45, 10, 70) 2011-01-31 23:51 : (?, 45.2, 9.9, 70.2)…… 2011-02-01 00:00 : (20.1,45.5,10.2,69.2)=> ? = 20.01
method 3 : encoding + interpolation
2011/03/01 15NTU CSIE iAgent Lab
Result
2011/03/01 16NTU CSIE iAgent Lab
baseline bagging(K=30, S=3000)(Tvent) 72.66%11m34s
70.43%
6 dimentions 93.21%45m12s
91.03%21m36s+ generate features 96.11%
1h32m50s93.40%40m14s+ missing value (encode) 96.07%
1h13m43s93.43%
+ missing value(interpolation) 99.79%54m41s
98.58%
+ missing value(encode + interpolation) 99.60%1h33m40s
96.10%
+ normalize 97.55% 94.05%
OutlinePrediction of AC State (revised)Definition of AC Waste AnalysisResult of AC Waste AnalysisThesis – Chapter 2
2011/03/01 17NTU CSIE iAgent Lab
Problem definition : energy(AC) waste analysis Input :
- state of motion sensor, mn={no, yes} - AC information
Output :- proportion of AC waste
2011/03/01 18NTU CSIE iAgent Lab
System overview
2011/03/01 19NTU CSIE iAgent Lab
motion sensorstate
AC statepredictorthermal comfort questionnaire
Tindoor Toutdoorthermal comfort equationand offset
AC information input
inputoutputproportion of AC wasteAC wasteanalysis
Condition of AC waste state of motion sensormn state of ACyn Tindoor ? TcomfortableRange waste or notN 0 higher NN 0 among NN 0 lower NN 1 higher Y N 1 among YN 1 lower YN 2 higher YN 2 among YN 2 lower YY 0 higher NY 0 among NY 0 lower NY 1 higher N?? Y 1 among NY 1 Lower N??Y 2 Higher Y Y 2 Among NY 2 lower Y
2011/03/01 20NTU CSIE iAgent Lab
OutlinePrediction of AC State (revised)Definition of AC Waste AnalysisResult of AC Waste AnalysisThesis – Chapter 2
2011/03/01 21NTU CSIE iAgent Lab
Problem
2011/03/01 23NTU CSIE iAgent Lab
AC statepredictorAC informationcollectorDataset Anothertestingdata
• Some place we did not have in the dataset yet.• Some patterns of the feature’s combination (dependent on time) in another testing data haven’t seen in the dataset .
error prediction
2011-01-01~2011-01-31all2010-12-17~2011-01-30R336 R324R204
Current condition feature :
- Tindoor, Hvent- new or old (building)- floor- room type- area
bagging - S=30000- K=5
2011/03/01 24NTU CSIE iAgent Lab
Condition of AC waste waste situation 1. mn = no and (yn = 1 or yn = 2) 2. mn = yes and yn = 2 and Tindoor < TcomfortableRange 3. mn = yes and yn = 2 and Tindoor > TcomfortableRange
2011/03/01 25NTU CSIE iAgent Lab
Proportion of AC waste waste situation 1. mn = no and (yn = 1 or yn = 2) 2. mn = yes and yn = 2 and Tindoor < TcomfortableRange 3. mn = yes and yn = 2 and Tindoor > TcomfortableRange
2011/02/21 26NTU CSIE iAgent Lab
place mn=no mn=yes yn=0 yn=1 yn=2 waste 1 waste 2 waste 3336_2 58% 42% 26% 61% 13% 36.7% 9.5% 0%
204_1 47% 53% 23% 70% 7% 33.8% 4.0% 0%
204_2 43% 57% 19% 39% 42% 35.8% 17.8% 0%
204_3 48% 52% 53% 44% 3% 23.9% 16.7% 0%
204_4 57% 43% 53% 41% 6% 28.2% 4.0% 0%
204_5 65% 35% 15% 13% 72% 57.2% 20.8% 0%
204_6 65% 35% 55% 41% 5% 31.9% 2.3% 0%
204 33% 67% 7% - - 31.0% - -
2011.01
OutlinePrediction of AC State (revised)Definition of AC Waste AnalysisResult of AC Waste AnalysisThesis – Chapter 2
2011/03/01 27NTU CSIE iAgent Lab