Progress Report

Progress ReportPresenter : Min-chia ChangAdvisor : Prof. Jane HsuDate : 2011 - 03 - 04

OutlinePrediction of AC State (revised)Definition of AC Waste AnalysisResult of AC Waste AnalysisDifference Control of Central ACSchedule and Goal

2011/03/04 2NTU 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 information


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

Time- R336 : 2010-12-18 ~ 2011-01-06- R204 : 2011-01-06 ~ 2011-01-17- R324 : 2011-01-20 ~ 2011-01-30

Current condition : continue collecting the new data into dataset 2011/03/04 5NTU CSIE iAgent Lab

Execution environment Weka Function: SVM

- Kernel: RBF Cross Validation: 3-fold

- In each iteration :


DatasetTrainingDataTestingData note : NEVER use testing data before

you predict.

Generate feature x=(Tindoor , Hindoor , Tvent , Hvent , Toutdoor , Houtdoor , context information)


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}zone 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}

total : 66 dimensions

Bagging (bootstrap aggregation)


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


……

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.

size = 51626


x=60dim K=1 K=2 K=3 K=4 K=5 K=10 K=30 K=100S=3 47.76%0m12s 52.80%0m21s 55.80%0m30s 58.13%0m46s 55.69%0m49s 67.49%1m36s 65.32%4m47s 68.65%16m14sS=300 62.03%0m18s 65.81%0m33s 71.87%0m48s 66.84%1m15s 74.32%1m22s77.50%2m37s 80.47%8m03s 81.00%26m41sS=1500 85.41%0m40s 84.60%1m13s 90.35%1m52s 85.11%2m40s 89.09%3m11s 90.15%7m41s 91.35%18m41s

91.61%65m13sS=3000 89.58%1m37s 87.76%2m26s 91.48%4m14s 91.47%8m30s 92.48%7m8s92.50%16m6s 93.45%44m04sS=7500 93.55%3m56s 92.45%7m30s 94.32%10m36s 93.90%16m51s

94.66%19m33s 94.99%37m33sS=15000 94.67%15m05s96.16%26m19s 95.25%41m41s 95.05%62m45s

95.42%74m23sS=22500 95.19%23m53s94.62%47m0s 95.60%74m22s

S=30000 95.30%37m07s94.77%72m12s

baseline:96.11%92m50sK=?, 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


Result


baseline bagging(K=?, S=?)(Tvent) 72.66%12m32s6 dim 93.21%45m12s6 dim +generate features (total : 60 dim)96.11%92m50s60 dim + missingValue : Encode 96.07%73m43s60 dim + missingValue : Interpolation 99.79%54m41s60 dim + missingValue : Encode, Interpolation 99.60%93m40s

60 dim + missingValue : Interpolation + normalize 97.55%



Problem definition : energy(AC) waste analysis Component 2 – AC state predictor:

- input : AC information- output : AC state ( yn={0, 1, 2} )

Component 3 – thermal comfort calculator :- input : thermal comfort questionnaire, Toutdoor- output : thermal comfort range

Component 4 – AC waste analysis : - input : mn , yn , thermal comfort range, Tindoor- output : proportion of AC waste


System overview


AC statepredictorthermal comfortcalculator

AC wasteanalysisACinformationthermal comfort questionnaire

Toutdoor

motion sensorstateACstate

thermal comfortrange Tindoor

proportion of AC waste

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 NY 1 among NY 1 lower NY 2 higher abnormalY 2 among NY 2 lower Y




Condition of AC 　 waste situation 1. mn = no and (yn = 1 or yn = 2) 2. mn = yes and yn = 2 and Tindoor < TcomfortableRange abnormal situation 1. mn = yes and yn = 2 and Tindoor > TcomfortableRange


Proportion of AC waste waste situation 1. mn = no and (yn = 1 or yn = 2) 2. mn = yes and yn = 2 and Tindoor < TcomfortableRange


place mn=no mn=yes yn=0 yn=1 yn=2 waste 1 waste 2 abnormal336_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





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


Thank you for listening !


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Progress Report