Progress Report Presenter : Min-chia Chang Advisor : Prof. Jane Hsu Date :

Outline  Prediction of AC State (revised)  Definition of AC Waste Analysis  Result of AC Waste Analysis  Difference Control of Central AC  Schedule and Goal 2011/03/042NTU CSIE iAgent Lab

Outline  Prediction of AC State (revised)  Definition of AC Waste Analysis  Result of AC Waste Analysis  Difference Control of Central AC  Schedule and Goal 2011/03/043NTU CSIE iAgent Lab

Data -label: ( OFF, ON no green, ON green ) define  y={0,1,2} -OFF : close -ON no green : T indoor < T userSetting, valve = OFF -ON green : T indoor > T userSetting, valve = ON -feature : define  ｘ, which is a vector -(T indoor, H indoor, T vent, H vent, T outdoor, H outdoor ) -context information 2011/03/044NTU CSIE iAgent Lab

Dataset  D={( ｘ n,y n )}, 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  Time -R336 : ~ R204 : ~ R324 : ~  Current condition : continue collecting the new data into dataset 2011/03/045NTU CSIE iAgent Lab

Execution environment  Weka  Function: SVM -Kernel: RBF  Cross Validation: 3-fold -In each iteration : 2011/03/046NTU CSIE iAgent Lab Dataset Training Data Testing Data note : NEVER use testing data before you predict.

Generate feature  x =( T indoor, H indoor, T vent, H vent, T outdoor, H outdoor, context information) 2011/03/047NTU CSIE iAgent Lab context datadimensionsvalue chilled water host3{0,1} chilled water temperature1integer rotation speed of pump1float new or old (building)2{0,1} floor5{0,1} room type6{0,1} zone6{0,1} area1Float day of the week7{0,1} weekday or weekend2{0,1} semester or vacation2{0,1} hour of the day24{0,1} total : 66 dimensions

Bagging (bootstrap aggregation) 2011/03/048NTU CSIE iAgent Lab Dataset Training Data Testing Data 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 data size = S re-sampling

Bagging 2011/03/049NTU CSIE iAgent Lab …… K training data size = S Training Data re-sampling y=0y=1 y=2 S/3 K fixed - If S decreases, then time decreases. size = 51626

2011/03/0110NTU CSIE iAgent Lab x= 60dim K=1K=2K=3K=4K=5K=10K=30K=100 S= % 0m12s 52.80% 0m21s 55.80% 0m30s 58.13% 0m46s 55.69% 0m49s 67.49% 1m36s 65.32% 4m47s 68.65% 16m14s S= % 0m18s 65.81% 0m33s 71.87% 0m48s 66.84% 1m15s 74.32% 1m22s 77.50% 2m37s 80.47% 8m03s 81.00% 26m41s S= % 0m40s % 1m13s 90.35% 1m52s 85.11% 2m40s 89.09% 3m11s 90.15% 7m41s 91.35% 18m41s 91.61% 65m13s S= % 1m37s % 2m26s 91.48% 4m14s 91.47% 8m30s 92.48% 7m8s 92.50% 16m6s 93.45% 44m04s S= % 3m56s 92.45% 7m30s 94.32% 10m36s 93.90% 16m51s 94.66% 19m33s 94.99% 37m33s S= % 15m05s 96.16% 26m19s 95.25% 41m41s 95.05% 62m45s 95.42% 74m23s S= % 23m53s 94.62% 47m0s 95.60% 74m22s S= % 37m07s 94.77% 72m12s baseline:96.11% 92m50s K=?, S=?

Process the missing value  missing value : T indoor, H indoor, T vent, H vent  processing method :  method 1 : encoding  e.g. : (?, ?, 15.2, ?) => (0, ?, 0, ?, 1, 15.2, 0, ?)  method 2 : interpolation (linear)  e.g. : :50 : (20, 45, 10, 70) :51 : (?, 45.2, 9.9, 70.2) …… :00 : (20.1,45.5,10.2,69.2) => ? =  method 3 : encoding + interpolation 2011/03/0411NTU CSIE iAgent Lab

Result 2011/03/0412NTU CSIE iAgent Lab baselinebagging(K=?, S=?) (T vent ) 72.66% 12m32s 6 dim 93.21% 45m12s 6 dim + generate features (total : 60 dim) 96.11% 92m50s 60 dim + missingValue : Encode 96.07% 73m43s 60 dim + missingValue : Interpolation 99.79% 54m41s 60 dim + missingValue : Encode, Interpolation 99.60% 93m40s 60 dim + missingValue : Interpolation + normalize 97.55%

Outline  Prediction of AC State (revised)  Definition of AC Waste Analysis  Result of AC Waste Analysis  Difference Control of Central AC  Schedule and Goal 2011/03/0413NTU CSIE iAgent Lab

Problem definition : energy(AC) waste analysis  Component 2 – AC state predictor: -input : AC information -output : AC state ( y n ={0, 1, 2} )  Component 3 – thermal comfort calculator : - input : thermal comfort questionnaire, T outdoor -output : thermal comfort range  Component 4 – AC waste analysis : -input : m n, y n, thermal comfort range, T indoor -output : proportion of AC waste 2011/03/0414NTU CSIE iAgent Lab

System overview 2011/03/04 15 NTU CSIE iAgent Lab AC state predictor thermal comfort calculator AC waste analysis AC information thermal comfort questionnaire T outdoor motion sensor state AC state thermal comfort range T indoor proportion of AC waste

Condition of AC waste state of motion sensor m n state of AC y n T indoor ? T comfortableRange waste or not N0higherN N0amongN N0lowerN N1higherY N1amongY N1lowerY N2higherY N2amongY N2lowerY Y0higherN Y0amongN Y0lowerN Y1higherN Y1amongN Y1lowerN Y2higherabnormal Y2amongN Y2lowerY 2011/03/0416NTU CSIE iAgent Lab

Outline  Prediction of AC State (revised)  Definition of AC Waste Analysis  Result of AC Waste Analysis  Difference Control of Central AC  Schedule and Goal 2011/03/0417NTU CSIE iAgent Lab

Condition of AC  waste situation 1.m n = no and (y n = 1 or y n = 2) 2.m n = yes and y n = 2 and T indoor < T comfortableRange  abnormal situation 1.m n = yes and y n = 2 and T indoor > T comfortableRange 2011/03/0418NTU CSIE iAgent Lab

Proportion of AC waste  waste situation 1.m n = no and (y n = 1 or y n = 2) 2.m n = yes and y n = 2 and T indoor < T comfortableRange 2011/02/2119NTU CSIE iAgent Lab placem n =nom n =yesy n =0y n =1y n =2waste 1waste 2abnormal 336_258%42%26%61%13%36.7%9.5%0% 204_147%53%23%70%7%33.8%4.0%0% 204_243%57%19%39%42%35.8%17.8%0% 204_348%52%53%44%3%23.9%16.7%0% 204_457%43%53%41%6%28.2%4.0%0% 204_565%35%15%13%72%57.2%20.8%0% 204_665%35%55%41%5%31.9%2.3%0% 20433%67%7%--31.0% _T ： 只用１，２的 ｖｏｔｅ 找出浪費的時 間週期 ……

Outline  Prediction of AC State (revised)  Definition of AC Waste Analysis  Result of AC Waste Analysis  Difference Control of Central AC  Schedule and Goal 2011/03/0420NTU CSIE iAgent Lab

Outline  Prediction of AC State (revised)  Definition of AC Waste Analysis  Result of AC Waste Analysis  Difference Control of Central AC  Schedule and Goal 2011/03/0421NTU CSIE iAgent Lab

Schedule and Goal  Schedule (March) -next step after result of AC waste analysis -definition of thesis part 2 -data aggregation of thesis part 2 -thesis writing : AC waste analysis (CH1, CH3) -(?) implementation of thesis part 2  Goal (this semester) -100 年 6 月順利口試 2011/03/0422NTU CSIE iAgent Lab

Thank you for listening ! 2011/03/0423NTU CSIE iAgent Lab