Diese Präsentation wurde erfolgreich gemeldet.
Wir verwenden Ihre LinkedIn Profilangaben und Informationen zu Ihren Aktivitäten, um Anzeigen zu personalisieren und Ihnen relevantere Inhalte anzuzeigen. Sie können Ihre Anzeigeneinstellungen jederzeit ändern.

Presentation03 27 03

908 Aufrufe

Veröffentlicht am

Veröffentlicht in: Technologie, Business
  • Als Erste(r) kommentieren

Presentation03 27 03

  1. 1. MEMS ‘Smart Dust Motes’ for Designing, Monitoring & Enabling Efficient Lighting MICRO Project Industry Sponsor General Electric Company; Global Research Center
  2. 2. Professor Alice Agogino, Faculty Advisor Jessica Granderson, Ph.D. Student Johnnie Kim, B.S. Student Yao-Jung Wen, Ph.D. Student Rebekah Yozell-Epstein, M.S. Student
  3. 3. Commercial Lighting • Electrical Consumption and Savings Potential • Advanced Commercial Control Technologies - Up to 45% energy savings possible with occupant and light sensors - Limited adoption in commercial building sector
  4. 4. Commercial Lighting • Problems With Advanced Control Technologies – Uncertainty is not considered --> sensor signals, estimation, target maintenance – Time is not considered, lost savings through demand reduction – All occupants are treated the same – Wires, retro-fit and commissioning
  5. 5. Intelligent Decision-Making with Motes • An intelligent decision algorithm allows: validation of sensor signals uncertainty in illuminance estimation differences in preference and perception peak load reduction/demand response • Smart dust motes potentially offer: wireless sensing at the work surface, increased sensing density, simpler retro-fitting and commissioning, wireless actuation, and an increased number of control points
  6. 6. BEST Lab Energy Research • Characterization, validation, and fusion of mote signals • Modeling the decision space for automatic dimming in large commercial office spaces (cubicles) • Benchmarking a specific decision space for switching and occupancy patterns, proposed smart lighting design • Determination of occupant preferences and perceptions for a specific decision space
  7. 7. Modeling the Decision Space • Goal is a model that can balance occupant preferences and perceptions with real-time electricity prices in daylighting decisions • Hierarchical problem breakdown – Local validation of sensor signals – Regional fusion of sensed data, actuation – Global optimization of regional decisions
  8. 8. Regional Influence Diagram
  9. 9. Immediate Work • Regional Decision-Making – Balance occupant preferences – Empirical occupant testing without windows to control for the effects of natural light – Incorporation of electricity prices for demand-responsive load shedding
  10. 10. Regional Decisions (no windows)
  11. 11. Future Work • Daylighting decisions – Glare, blinds – Natural/artificial light contributions – Contrast • Design of a global value function – Optimal combination of regional decisions
  12. 12. Features of Sensor Validation and Fusion for Sensor Networks • Purpose – Provide reliable information of current environment for decision-making – Feed appropriate value back to the control system • Main Idea – Fuse sensor of the same kind into one or more reliable virtual sensor – Fuse disparate sensors
  13. 13. Research Goals • Characterize mote sensors • Find and construct the most suitable sensor validation and fusion algorithm for sensor networks • Build algorithm for sensor locating based on the result of sensor validation and fusion.
  14. 14. Purpose of Sensor Validation • Noise rejection • Fault detection – Sensor failure – Process failure – System failure • Ultimate purpose To provide the most reliable data for fusing
  15. 15. Methodology for Sensor Validation 1. Signal check Sensed data 2. Absolute limits Signal output check check Sensor Absolute limits check 3. System feature performance limits Previous Performance limits check value check Expect behavior check 4. Expected behavior check Correlation check 5. Empirical Fusion procedure correlation check
  16. 16. Possible Methodology for Sensor Fusion • Fuzzy Approach • Kalman filter • Bayesian network • Neural network
  17. 17. Sensor Fusion and Validation Sensor readings Supervisory controller Diagnosis Calculate fused value using old predicted value for validation gate and incoming readings Sensor Fusion Fused value Calculate new predicted Sensor Validation value using fused value Controller Machine Level Controller Decision-making system Sensor Readings Algorithm for sensor Architecture for Sensor Validation validation and fusion and Sensor Fusion
  18. 18. The Mote Processor and Radio Platform • Atmega 128L processor (4MHz) • 916MHz transceiver • 100 feet maximum radio range • 40Kbits/sec data rate
  19. 19. The Mote Sensor Board
  20. 20. The Mote Sensor Board Microphone Magnetometer Panasonic Honeywell WM-62A Hmc1002 Thermistor Buzzer Panasonic Sirius ERT-J1VR103J PS14T40A (missing) Light Sensor Clairex CL9P4L Accelerometer Analog Devices ADXL202JE
  21. 21. The Mote Other Accessories • Basic Sensorboard This board has two sensors: temperature photo and is capable of integrating other kinds of sensors on it. • Interface Board Programming each mote platform via parallel port. Aggregation of sensor network data onto a PC via serial port.
  22. 22. Example I Analyzing of Old Cory Hall Data Mote node_id 6174 Mote Location and Environment
  23. 23. Example I Analyzing of Old Cory Hall Data Mote node_id 6174 Mote Location and Environment
  24. 24. Example I (contd.) Analyzing of Old Cory Hall Data Mote node_id 6174 Light Readings and Temperature readings 5/24/01~5/31/01
  25. 25. Example I (contd.) Analyzing of Old Cory Hall Data Mote node_id 6174 Possible failure of light sensor Possible failure of both light and temperature sensor Light Readings and Temperature readings 5/24/01~5/31/01
  26. 26. Example II Analyzing of Old Cory Hall Data Mote node_id 6190 & 6191 in Room 490 Sensor Readings in Cory Hall 490 5/17/01~5/22/01
  27. 27. Example II (Contd.) Analyzing of Old Cory Hall Data Mote node_id 6190 & 6191 in Room 490 Fusion of Light Reading of 5/17 Using Dr. Goebel’s FUSVAF Algorithm
  28. 28. Potential Difficulties: Validation and Fusion • There is not a specific sensor on the sensor board for sensing occupancy • Error of mapping sensor signals to physical readings due to the non-linearity and sensitivity of each sensor element • The sampled data for the same time stamps might be received at different time due to wireless communication • Only one sensor per board functions at any given time
  29. 29. Plans for the Next Two Months • Setup the software and hardware to actuate the smart motes on hand • Characterize the motes signals • Collect data of target office space using one or several motes • Characterize motes failure patterns for individual motes • Build algorithms for feature identification and extraction • Search for the accurate and efficient way to sense occupancy
  30. 30. Plans for the Next Six Months • Build up mote sensor networks in the target office space • Benchmark test the networks • Characterize motes failure patterns for mote networks • Evaluate appropriate validation and fusion algorithms • Determine best locations for motes
  31. 31. Plans for the Future • Implement the mote validation and fusion algorithm to real time validating and fusing • Refine the mote validation and fusion algorithm • Evaluate the possibility of using motes to actuate dimming ballast directly
  32. 32. Benchmarking Research Goals • Verify the need for a smart lighting system based on human interactions with their environment • Develop design guidelines for a smart lighting system • Propose a smart lighting system for the BEST Lab, (6102 Etch.)
  33. 33. Benchmarking Research Deliverables • Benchmark the current switching and occupancy patterns in the BEST Lab • Discuss potential energy savings based on the results of this benchmarking • Perform a usability study to determine user preferences with respect to smart lighting • Propose a system that will personalize lighting based on occupancy and save on electricity costs
  34. 34. Occupancy in Work Area Average Total Occupancy vs. Time of Day 4 3.5 3 Average occupancy (people) Wednesday 2.5 Thursday Friday 2 Saturday 1.5 Sunday Monday 1 Tuesday 0.5 0 -1 4 9 14 19 24 -0.5 Time of day (military time)
  35. 35. Occupancy in Conference Area Average Conference Area Occupancy 3.5 3 2.5 Average Occupancy Wednesday 2 Thursday Friday Saturday 1.5 Sunday Monday 1 Tuesday 0.5 0 0 5 10 15 20 25 -0.5 Time of Day (military time)
  36. 36. Switching Patterns in BEST Lab Switching Patterns 120.0 100.0 Probability That Light Will Be On 80.0 Monday Tuesday 60.0 Wednesday Thursday Friday 40.0 Saturday Sunday 20.0 0.0 0 5 10 15 20 25 -20.0 Time of Day (military time)
  37. 37. Potential Energy Savings • Calculate current energy usage in lab • Calculate energy usage for lights only being used when and where they are needed • Compare current and potential costs
  38. 38. Usability Issues • What level of manual control and override will users need to feel comfortable with the system? • How will users enter personal lighting preferences into the system and when (initially or once a problem is detected)?
  39. 39. Occupant Preferences and Perceptions • Goal: Determine the illuminance ranges over which occupants perceive the lighting at their desk to be – too bright, – too dark, – or just right
  40. 40. Empirical Preference Testing • Method: Perform multiple tests on individuals at their respective workstations • Equipment: – 4-light fluorescent shop light – Dimmable electronic ballast – 0-10 VDC source – PVC Piping framework
  41. 41. Experiment flowchart Dimmable 0-10 V Variable User’s electronic variable DC illuminance perception ballast
  42. 42. Experimental Setup • A desktop apparatus that provides lighting 6-8 ft. directly above the work surface 6-8 ft.
  43. 43. Light Fixturing Detail chain connections 4-light fixture
  44. 44. Future Energy Work • Extension to intelligence HVAC control • Agent-based technology for actuation • Further personalization for individual spaces