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Progress Report

Progress Report. Presenter : Min- chia Chang Advisor : Prof. Jane Hsu Date : 201 1 - 03 - 04. Outline. Prediction of AC State (revised) Definition of AC Waste Analysis Result of AC Waste Analysis Difference Control of Central AC Schedule and Goal. Outline.

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Progress Report

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  1. Progress Report Presenter : Min-chia Chang Advisor : Prof. Jane Hsu Date : 2011 - 03 - 04

  2. Outline • Prediction of AC State (revised) • Definition of AC Waste Analysis • Result of ACWaste Analysis • Difference Control of Central AC • Schedule and Goal NTU CSIE iAgent Lab

  3. Outline • Prediction of AC State (revised) • Definition of AC Waste Analysis • Result of ACWaste Analysis • Difference Control of Central AC • Schedule and Goal NTU CSIE iAgent Lab

  4. 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 NTU CSIE iAgent Lab

  5. 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 • 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 NTU CSIE iAgent Lab

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

  7. Generate feature • x=(Tindoor , Hindoor ,Tvent ,Hvent ,Toutdoor ,Houtdoor , context information) total : 66 dimensions NTU CSIE iAgent Lab

  8. Bagging (bootstrap aggregation) Training Data re-sampling Dataset Ktrainingdata Testing Data …… size = S size =S size = S K=?, S=? • K fixed • - If S decreases, thentime decreases. • S fixed • - If K increases, then the result of the vote is more convinced. NTU CSIE iAgent Lab

  9. Bagging • K fixed • - If S decreases, thentime decreases. Ktrainingdata y=0 y=1 y=2 Training Data …… size = S size =S size = S S/3 S/3 S/3 re-sampling size = 51626 NTU CSIE iAgent Lab

  10. K=?, S=? baseline:96.11% 92m50s NTU CSIE iAgent Lab

  11. 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 NTU CSIE iAgent Lab

  12. Result NTU CSIE iAgent Lab

  13. Outline • Prediction of AC State (revised) • Definition of AC Waste Analysis • Result of ACWaste Analysis • Difference Control of Central AC • Schedule and Goal NTU CSIE iAgent Lab

  14. 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 NTU CSIE iAgent Lab

  15. System overview motion sensor state AC waste analysis AC state predictor proportion of AC waste AC information AC state thermal comfort calculator thermal comfort questionnaire thermal comfort range Tindoor Toutdoor NTU CSIE iAgent Lab

  16. Condition of AC waste NTU CSIE iAgent Lab

  17. Outline • Prediction of AC State (revised) • Definition of AC Waste Analysis • Result of ACWaste Analysis • Difference Control of Central AC • Schedule and Goal NTU CSIE iAgent Lab

  18. Condition of AC • waste situation • mn = no and (yn = 1 or yn = 2) • mn = yes and yn = 2 and Tindoor < TcomfortableRange • abnormal situation • mn = yes and yn = 2 and Tindoor > TcomfortableRange NTU CSIE iAgent Lab

  19. Proportion of AC waste • waste situation • mn = no and (yn = 1 or yn = 2) • mn = yes and yn = 2 and Tindoor < TcomfortableRange 2011.01 找出浪費的時間週期…… 204_T：　 只用１，２的 ｖｏｔｅ NTU CSIE iAgent Lab

  20. Outline • Prediction of AC State (revised) • Definition of AC Waste Analysis • Result of ACWaste Analysis • Difference Control of Central AC • Schedule and Goal NTU CSIE iAgent Lab

  21. Outline • Prediction of AC State (revised) • Definition of AC Waste Analysis • Result of ACWaste Analysis • Difference Control of Central AC • Schedule and Goal NTU CSIE iAgent Lab

  22. 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 NTU CSIE iAgent Lab

  23. Thank you for listening! NTU CSIE iAgent Lab

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