Post on 04-Jun-2018
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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
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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
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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
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Intelligent Decision-Making
with Motes An intelligent decision algorithm allows:
validation of sensor signalsuncertainty in illuminance estimation
differences in preference and perception
peak load reduction/demand response
Smart dust motes potentially offer:
wireless sensing at the work surface, increasedsensing density, simpler retro-fitting and
commissioning, wireless actuation, and an
increased number of control points
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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
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Modeling the Decision Space
Goal is a model that can balance
occupant preferences and perceptionswith 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
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Immediate Work
Regional Decision-Making
Balance occupant preferences
Empirical occupant testing without
windows to control for the effects ofnatural light
Incorporation of electricity prices for
demand-responsive load shedding
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Future Work
Daylighting decisions
Glare, blinds
Natural/artificial light contributions
Contrast Design of a global value function
Optimal combination of regional
decisions
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Features of Sensor Validation
and Fusion for Sensor Networks Purpose
Provide reliable information of currentenvironment for decision-making
Feed appropriate value back to the control
system
Main Idea
Fuse sensor of the same kind into one ormore reliable virtual sensor
Fuse disparate sensors
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Research Goals
Characterize mote sensors
Find and construct the most suitablesensor validation and fusion algorithm
for sensor networks Build algorithm for sensor locating
based on the result of sensor validation
and fusion.
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Purpose of Sensor Validation
Noise rejection
Fault detection Sensor failure
Process failure
System failure
Ultimate purpose
To provide the most reliable data for fusing
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Methodology for Sensor
Validation1. Signal check
2. Absolute limitscheck
3. System
performance limitscheck
4. Expected behavior
check5. Empirical
correlation check
Performance limits check
Sensed data
Expect behavior check
Correlation check
Absolute limits check
Signal output check
Fusion procedure
Previousvalue
Sensor
feature
ibl h d l f
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Possible Methodology for
Sensor Fusion Fuzzy Approach
Kalman filter
Bayesian network
Neural network
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Sensor Fusion and Validation
Calculate fused value using oldpredicted value for validation
gate and incoming readings
Calculate new predicted
value using fused value
Fused value
Sensor readings
Controller
Decision-making system
Supervisory controller
Sensor Validation
Sensor Fusion
Sensor Readings
Diagnosis
Machine Level
Controller
Algorithm for sensor
validation and fusionArchitecture for Sensor Validation
and Sensor Fusion
h
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The Mote
Processorand Radio Platform
Atmega 128L processor (4MHz)
916MHz transceiver
100 feet maximum radio range 40Kbits/sec data rate
Th M
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The Mote
Sensor Board
Th M
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The Mote
Sensor BoardMicrophone
Panasonic
WM-62A
ThermistorPanasonic
ERT-J1VR103J
Light Sensor
Clairex
CL9P4L
Magnetometer
Honeywell
Hmc1002
Accelerometer
Analog DevicesADXL202JE
Buzzer
Sirius
PS14T40A
(missing)
Th M t
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The Mote
Other Accessories Basic Sensorboard
This board has twosensors:temperature
photoand is capable ofintegrating other kinds ofsensors on it.
Interface BoardProgramming each mote
platform via parallel port.
Aggregation of sensor
network data onto a PC via
serial port.
Example I
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Example IAnalyzing of Old Cory Hall Data
Mote node_id 6174
Mote Location and
Environment
Example I
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Example IAnalyzing of Old Cory Hall Data
Mote node_id 6174
Mote Location and
Environment
Example I (contd )
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Example I (contd.)Analyzing of Old Cory Hall Data
Mote node_id 6174
Light Readings and
Temperature readings
5/24/01~5/31/01
Example I (contd )
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Example I (contd.)Analyzing of Old Cory Hall Data
Mote node_id 6174
Light Readings and
Temperature readings
5/24/01~5/31/01
Possible
failure of
light sensor
Possible failure of
both light and
temperature sensor
Example II
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Example IIAnalyzing 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
Example II (Contd )
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Example II (Contd.)Analyzing of Old Cory Hall Data
Mote node_id 6190 & 6191 in Room 490
Fusion of LightReading of 5/17
Using Dr. Goebels
FUSVAF Algorithm
Potential Difficulties:
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Potential Difficulties:
Validation and Fusion There is not a specific sensor on the
sensor board for sensing occupancy Error of mapping sensor signals tophysical 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
Plans for the Next
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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 featureidentification and extraction
Search for the accurate and efficient way
to sense occupancy
Plans for the Next
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Plans for the Next
Six Months Build up mote sensor networks in
the target office space Benchmark test the networks
Characterize motes failure patternsfor mote networks
Evaluate appropriate validation andfusion algorithms
Determine best locations for motes
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Plans for the Future
Implement the mote validation and
fusion algorithm to real timevalidating and fusing
Refine the mote validation andfusion algorithm
Evaluate the possibility of using
motes to actuate dimming ballast
directly
Benchmarking Research
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Benchmarking Research
Goals Verify the need for a smart lighting
system based on human interactionswith their environment
Develop design guidelines for a smartlighting system
Propose a smart lighting system for the
BEST Lab, (6102 Etch.)
Benchmarking Research
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Benchmarking Research
Deliverables Benchmark the current switching and
occupancy patterns in the BEST Lab
Discuss potential energy savings based onthe results of this benchmarking
Perform a usability study to determineuser preferences with respect to smartlighting
Propose a system that will personalizelighting based on occupancy and save onelectricity costs
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Occupancy in Work Area
Average Total Occupancy vs. Time of Day
-0.5
0
0.5
1
1.5
2
2.5
3
3.5
4
-1 4 9 14 19 24
Time of day (military time)
Averageoccupancy(people)
Wednesday
Thursday
Friday
Saturday
Sunday
Monday
Tuesday
Occupancy in
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Occupancy in
Conference AreaAv erage Conference Area Occupancy
-0.5
0
0.5
1
1.5
2
2.5
3
3.5
0 5 10 15 20 25
Time of Day (military time)
AverageOcc
upancy
Wednesday
Thursday
Friday
Saturday
Sunday
Monday
Tuesday
Switching Patterns
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Switching Patterns
in BEST LabSwitching Patterns
-20.0
0.0
20.0
40.0
60.0
80.0
100.0
120.0
0 5 10 15 20 25
Time of Day (military t ime)
ProbabilityThatL
ightWillBeOn
Monday
Tuesday
WednesdayThursday
Friday
Saturday
Sunday
P i l E S i
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Potential Energy Savings
Calculate current energy usage in lab
Calculate energy usage for lights onlybeing used when and where they are
needed Compare current and potential costs
U bilit I
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Usability Issues
What level of manual control and
override will users need to feelcomfortable with the system?
How will users enter personal lightingpreferences into the system and when
(initially or once a problem is detected)?
Occupant Preferences and
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p
Perceptions Goal: Determine the illuminance
ranges over which occupants perceivethe lighting at their desk to be
too bright,
too dark,
or just right
E i i l P f T ti
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Empirical Preference Testing
Method:
Perform multiple tests on individuals attheir respective workstations
Equipment:
4-light fluorescent shop light
Dimmable electronic ballast
0-10 VDC source PVC Piping framework
E i t fl h t
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Experiment flowchart
0-10 V
variable DC
Dimmable
electronic
ballast
Variable
illuminance
Users
perception
E i t l S t
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Experimental Setup
A desktop apparatus
that provides lighting6-8 ft. directly above
the work surface6-8 ft.
Light Fixturing Detail
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Light Fixturing Detail
4-light fixture
chain
connections
Future Energy Work
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Future Energy Work
Extension to intelligence HVAC
control Agent-based technology for
actuation Further personalization for
individual spaces