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Presenter : Min- Chia Chang Advisor : Prof. Jane Hsu Date : 201 1 - 06 -30

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 : 201 1 - 06 -30. Outline. Introduction System Analysis Conclusion .

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Presenter : Min- Chia Chang Advisor : Prof. Jane Hsu Date : 201 1 - 06 -30

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  1. 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

  2. Outline • Introduction • System • Analysis • Conclusion NTU CSIE iAgent Lab

  3. Energy Saving • Reason • Policy NTU CSIE iAgent Lab

  4. Power Consumptionin a Building (source : Continental Automated Buildings Association, CABA) NTU CSIE iAgent Lab

  5. Architecture of Central A/C System • Chilled water host • Evaporator • Condenser • Other devices • Pump • Cooling tower NTU CSIE iAgent Lab

  6. Energy Conservation for Central A/C System • Device setting • The setting ofthe 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

  7. 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 - April 2011 ) • 3,693.8KHW/day • 40.88% of the total (source : NTU 校園數位電錶監視系統)

  8. Abnormal A/C State in NTU CSIE • Ideal power consumption KWH NTU CSIE iAgent Lab

  9. Abnormal A/C State in NTU CSIE • Real power consumption A/C is turned off ? KWH NTU CSIE iAgent Lab

  10. 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

  11. Abnormal A/C State in NTU CSIE Hot Cold NTU CSIE iAgent Lab

  12. Outline • Introduction • System • Analysis • Conclusion NTU CSIE iAgent Lab

  13. System Overview NTU CSIE iAgent Lab

  14. Wireless Sensor Network NTU CSIE iAgent Lab

  15. Sensors • Platform : Taroko • Temperature and humidity sensor : SHT11 • Infrared motion sensor NTU CSIE iAgent Lab

  16. Nodes in the Sensor Network • Sender • (temperature, humidity, ID) • (preamble, motion value, ID) • Receiver • Data saving : 1 minute • Relay NTU CSIE iAgent Lab

  17. Collection Unit • Room : divide into zones according to A/C controller • Environmental data • temperature and humidity • vent • indoor • occupancy state vent indoor motion sensor NTU CSIE iAgent Lab

  18. Deployment • One server per floor (1F to 5F) • Relays deploy around the corridors NTU CSIE iAgent Lab

  19. Deployment • Room • Class room : R104 • Computer class room : R204 • Professor room : R318 • Laboratory : R336 • Seminar room : R324, R439, R521 NTU CSIE iAgent Lab

  20. A/C Mode Recognition NTU CSIE iAgent Lab

  21. A/C Mode • Mode • Off mode : blower= off • Venting mode : blower = on , valve = off • Cooling mode : blower= on , valve = on power A/C temperature setting A/C mode wind velocity ≧ indoor temperature ＜ Off Venting Cooling NTU CSIE iAgent Lab

  22. A/C Mode Recognition • GOAL : • Using machine learning to build the model for recognizing the A/C mode • ASSUMPTION : • People control the A/C mode part of according to the weather • INPUT : • Feature vector • OUTPUT : • A/C mode NTU CSIE iAgent Lab

  23. Features NTU CSIE iAgent Lab

  24. Annotation • Method 1 • Control on purpose • Method 2 • Record by camera NTU CSIE iAgent Lab

  25. Dataset NTU CSIE iAgent Lab

  26. Evaluation • Each zone builds a model • 4-fold cross validation • Constraint : couldn’t collect all the weather patterns • The weather pattern in testing data doesn’t exist in training data NTU CSIE iAgent Lab

  27. Steps of the Experiment 2 • Cluster • Algorithm : k-means (k=4) • Feature: outdoor temperature, outdoor humidity • Leave-one-out Cross Validation 336_2 outdoor humidity outdoor temperature NTU CSIE iAgent Lab

  28. Preprocessing • Missing data treatment • Encoding • Recognize the data is missing or not • Linear interpolation • All missing data are temperature and humidity • If the first or last data is missing data • Replace with global mean after the interpolation • Normalization • Min-max normalization : [0,1] • It prevents features with large scale biasing the result NTU CSIE iAgent Lab

  29. Experiment Result • Result • Each zone’s accuracy in experiment 2 is higher than 85% • Each zone’s accuracy in experiment 1 is higher than experiment 2 • 204_5 has the highest accuracy (only 2 label) NTU CSIE iAgent Lab

  30. Thermal Comfort Calculation NTU CSIE iAgent Lab

  31. Thermal Comfort Calculation • GOAL : • Find thermal comfort range of the environment • INPUT : • Questionnaire • OUTPUT : • Thermal comfort range NTU CSIE iAgent Lab

  32. 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

  33. Thermal Sensation Scale • Thermal sensation scale [ASHRAE Standard 55] • Adaptive method to get PMV • Constraints • Metabolic rate : 1.0Met - 2.0Met • Clothing insulation : ≦ 1.5 Clo • Comfortable or not • -1, 0, +1 : yes • -2, -3, +2, +3 : no NTU CSIE iAgent Lab

  34. Thermal Comfort - Linear Regression • Field survey • Collect thermal sensation vote (TSV) • Outdoor temperature has the highest relevance with thermal comfort • TC=17.8+0.31TO(Worldwide) [deDearand Brager 1998] • TC=18.3+0.158TO(Hong Kong)[Mui and Chan 2003] • TC=15.5+0.29TO(Taiwan)[Lin et al. 2008] NTU CSIE iAgent Lab

  35. Questionnaire • Thermal sensation scale :{-3, -2, -1, 0 ,+1, +2, +3} • Direct question : {comfortable, not comfortable} • Metabolic rate : {after sport, static activity} • Clothing insulation : {sleeveless, shirt-sleeve, long-sleeve, thick coat} VALID ! NTU CSIE iAgent Lab

  36. Data Collection NTU CSIE iAgent Lab

  37. Result • Linear regression equation • TC=20.6+0.107TO • TC=17.8+0.31TO(Worldwide) • TC=18.3+0.158TO(Hong Kong) • TC=15.5+0.29TO(Taiwan) NTU CSIE iAgent Lab

  38. 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

  39. Thermal Comfort Range • Regression • Indoor temperature • Mean thermal sensation vote (PMV) during each ℃ 2.67 NTU CSIE iAgent Lab

  40. Thermal Comfort Range 2.67 NTU CSIE iAgent Lab

  41. A/C State Evaluation NTU CSIE iAgent Lab

  42. 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

  43. A/C State people in the room A/C = turned on A/C= cooling mode N N N Y Y Y normal abnormal indoor temperature ? comfort range normal lower within higher abnormal abnormal normal NTU CSIE iAgent Lab

  44. Outline • Introduction • System • Analysis • Conclusion NTU CSIE iAgent Lab

  45. Analysis of Abnormal A/C States Abnormal A/C States Detecting System normal/ abnormal useful information history data analysis User NTU CSIE iAgent Lab

  46. Target Room • Room • Class room : R104 • Computer class room : R204 • Professor room : R318 • Laboratory : R336 • Seminar room : R324, R439, R521 NTU CSIE iAgent Lab

  47. Valid Data • From January 2011 to May 2011 NTU CSIE iAgent Lab

  48. Professor Room - R318 weekday distribution during a week weekend NTU CSIE iAgent Lab

  49. Seminar Room – R439 distribution during a week weekday weekend NTU CSIE iAgent Lab

  50. Class Room – R104 distribution during a week weekday weekend NTU CSIE iAgent Lab

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