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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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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.)


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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

presentation032703-1234028706714461-1

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