Lighting and Medical Personalization:

Optimizing Efficiency and Customer Satisfaction Alice M. Agogino Roscoe and Elizabeth Hughes Professor of Mechanical Engineering

Researchers

• Professor Alice Agogino, Faculty Advisor • Marisela Avalos, MS/PhD student • Matt Dubberley, MS student, Fall 2003 • Jessica Granderson, PhD student • Mary Haile, undergraduate student • Johnnie Kim, undergraduate student • Catherine Newman, MS/PhD student • Jaspal Sandhu, PhD student • Yao-Jung Wen, MS/PhD student • Rebekah Yozell-Epstein, MS student, Spring 2003

Motivation

• Indoor Environmental Quality: Lighting – Increased productivity – Increased quality of experience • Energy Efficiency – Increased importance world-wide – Impact on pollution, global warming, expense

Motivation: Commercial Lighting

• Electrical Consumption and Savings Potential – 2/3 of electricity generated in US is for buildings – Lighting consumes 40% of the electricity used in buildings • Advanced Commercial Control Technologies - Up to 45% energy savings possible with occupant and light sensors - Limited adoption in commercial building sector

Background: Commercial Lighting

• Problems With Advanced Control Technologies – Simple control algorithms dim the lights in direct proportional response to the sensor signal: uncertainty is not considered --> sensor signals, estimation/maintenance of desktop illuminance – Time of day/week is not considered, lost savings through demand reduction – All occupants are treated the same in spite of the vast differences in perception and preference that exist between individuals

Background: Commercial Lighting

• Problems With Advanced Control Technologies – Systems are hard-wired into the line electricity of the building making retrofitting expensive and prohibitive – Required calibration and installation expertise make successful commissioning difficult – Algorithms can result in annoyance to the users through inappropriate switching or speed of switching

Research Goals

• • Increased energy savings through the implementation of demand-responsive decision-making – Increased user satisfaction with the system Personalized, improved decision-making; balancing conflicting preferences/ perceptions among individuals sharing a common light source/switch – Improved maintenance of target illuminance at the worksurface – Increased user satisfaction with the system

Benchmark Occupancy

Average Total Occupancy vs. Time of Day

4 3.5

1 0.5

-1 0 -0.5

3 2.5

2 1.5

4 9 14

Time of day (military time)

19 24 Wednesday Thursday Friday Saturday Sunday Monday Tuesday

Potential Energy Savings for Office Building

Hourly Charge (per kWh per month) Daily Office Area Charge Daily Conference Area Charge Daily Hallway Charge Potential Daily Office Charge Potential Daily Conference Charge Potential Daily Hallway Charge Total Annual Charge Potential Annual Charge Potential Annual Savings Summer $0.08915

$124 $15 $7 $64 $1 $6 $48,363 $23,725 $24,638 Winter $0.07279

$101 $12 $6 $53 $1 $5

User Studies

• Survey Feedback • 54% want a dimly lit view of rest of room • 59% require slightly different light levels throughout the day (desk lamp) • 32% want automatic overhead lights with override and manual task lamps • 77% like same lighting throughout the day • 73% want to rely on default settings at first and then enter preferences later

Life Cycle Assessment of the Intelligent Lighting System using the Distributed Mote Network • MS Project, Matt Dubberly • Goals: – To evaluate the environmental impacts associated with implementing the proposed Intelligent Lighting – Compare the electricity saving benefits of the Intelligent Lighting System to the environmental burdens associated with implementing the system.

– Provide insight for design choices, such as what type of battery should be used or which materials and components should be minimized

• The negative environmental impacts of the proposed Intelligent Lighting System range from 17 to 344 times smaller than that of conventional lighting systems for the different environmental impact categories.

• The components that contribute the most to the system impact are – Mote printed circuit board – Mote integrated circuit – Lithium battery – Ballast housing paint – The silicon steel and copper in the ballast transformer and inductor 100% 80% 60% 40% 20% 0% Ac id ifi ca tio n Ec ot ox ic ity Eu tro ph ic at io n Fo ss il Fu G el lo ba l W H ar um m an in H g H ea lth um C an an H ce ea r lth N on ca nc O zo er ne D ep Ph le ot tio oc n he m ic al S m og W at er U se

Impact Category

Transportation Printed Circuit Board (mote) Plastic Casing (mote) Paint (ballast) Metal Forming (ballast Housing) Li Battery (mote) Integrated Circuit (mote) Integrated Circuit (ballast) Ballast Transformer/Inductor (Si steel) Ballast Transformer/Inductor (Cu) Ballast Housing

Intelligent Decision-Making and Smart Dust Motes – Granderson

• An intelligent decision algorithm allows: – Validation & fusion of sensor signals – Differences in user preferences and perceptions – Peak load reduction/demand responsiveness • Influence diagrams allow: – Real-time decision-making and control – Uncertainty in knowledge (sensor values and non-deterministic relationships) – Ability to represent complex interdependencies – Rules for combining evidence, based on rigorous probability theory or fuzzy logic

Intelligent Decision-Making and Smart Dust Motes

• 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

Intelligent Framework: Modeling the Decision Space

• Initially models demand-responsive and personalization aspects of the problem.

• Variables Included – Day, time, electricity price, workstation occupancy, sensed workstation occupancy, actuation decision, task type, resulting illuminance (following actuation), resulting perception of the occupant • Constants Included – Preferred ideal illuminance, min/maximum actuation, ideal reward, vacancy penalty/reward

Regional Decision Space with Local/Individual Factors

Intelligent Framework: Modeling the Decision Space

• After developing the personalized, demand-responsive decision model, daylighting factors were incorporated • Variables added – Month, weather (cloudy), latitude, solar azimuth and altitude, room geometry, sensed and true solar contribution to to the region, solar contribution to the ith worksurface

Empirical Preference Testing

• Purpose: to identify the illuminance ranges over which occupants find the lighting to be ideal, too dark, and too bright at their personal workstations • This gives us – conditional probabilities required for the decision model – information to use in the value function

Empirical Preference Testing

• Results – Probabilistic conditional preference data of the form P(Illuminance|Perception), P(Perception), that can be used in the personalized, preference balancing control model

Preference Testing - Results

• Paper-based tasks required significantly more light than computer-based tasks

Preference Testing - Results

• No illuminance range proved to be ideal for all four occupants, even though all share the same switch (computer histogram)

Preference Testing - Results

• No illuminance range proved to be ideal for all four occupants, even though all share the same switch (paper histogram)

Preference-balancing Value Function

• Goal is to create a function that: – heavily favors meeting the ideal illuminances of those present – heavily favors turning the lights off/min in the absence of occupants – heavily penalizes turning the lights on/max in the absence of occupants – assigns a value of difference-from-ideal for each occupant present, and each possible actuation decision

Future Research – Evaluation of Research Goals

• Evaluation of preference-balancing value function – computer simulation • Evaluation of target illuminance maintenance – hardware simulation • Evaluation of energy savings achieved with demand-responsiveness – computer simulation • Evaluation of user satisfaction w/ the system - implementation in a daylighted test space, complimented with user surveys

Validation of Motes and Network

• Construct & test architectures for mote sensor networks in target office spaces • Characterize the motes signals and failure patterns • Develop appropriate validation and fusion algorithms – Calibration on mote sensors – Evaluate fuzzy & probabilistic fusion algorithm on sensor networks

Illuminance Calibration

• Hardware/Experimental Set-up – Light sources: • Fluorescent room light • Incandescent desk lamp (75W bulb) .

• Halogen floor lamp.

– Minolta T-10 illuminance meter

Illuminance Calibration

y

   (940 1023)  5

x

4  4.6397 10  2

x

3  73.118

x

2  50710

x

 7

y

 (780 940)  5

x

3 

y

 (0 780)  12

x

5   2

x

2  34.947

x

 7512.4

 9

x

4   6

x

3 General Operating Range

Illuminance Calibration

Sensor readings v.s. Illuminance 3000 2500 2000 1500 1000 500 0 0 mote01 mote02 mote03 mote04 mote05 mote06 mote07 mote08 mote09 mote10 mote11 mote12 200 General Operating Range 400 600 Sensor readings 800 1000 1200

Illuminance Calibration

3000 2500 Curve fitting for readings from all sensors Sensor value Fitting curve over all range Fitting curve over general operation range 2000 1500

y

 6.726



e

0.005484

x

1000 500 General Operation Range 0 0 200 400 600 Sensor reading 800

y

 1000 1200  10 

x

4.297

Probability Distribution of the Mapping Curve

• Illustration of probability distribution when mapped readings are around 500 lux Distribution of data between reading 854 and 895 (876 maps to 500 lux) 0.25

0.2

0.15

0.1

0.05

0 0 500 1000 1500 Illuminance (lux) 2000 2500

Temperature Calibration

Sensor readings v.s. Temperature 80 70 60 50 40 30 20 10 0 0 100 200 300 400 Sensor readings 500 600 700

Temperature Calibration

Sensor readings v.s. Temperature 90 80 70 60 50 40 30 20 10 0 -10 0 Thermometer on MICA sensor board Thermometer on basic sensor board 100 200 300 400 500 600 Sensor readings 700 800 900 mote01 mote02 mote03 mote04 mote05 mote06 mote07 mote08 mote09 mote10 mote11 mote13 mote14 1000

Fuzzy Validation and Fusion on BESTnet v1.0

• Real-time Fuzzy Sensor Validation And Fusion (FUSVAF)* algorithm

Raw sensor readings

Z -1 Z -1

Calculate new predicted value

Z -1

Determine confidence values for sensor readings

Z -1

Calculate new α Fuse sensor readings Fused value for machine level controller/ supervisory controller

Feasibility of Using Accelerometer as Occupancy Sensor • Hardware setup: mote13

x

receiver mote14 mote13 mote14 receiver

y

Motes as Decentralized Autonomous Agents – Sandhu

• Agents with collective intelligence may be more efficient than centralized control.

• Model the motes as a collection of intelligent agents that share the same global utility function.

• Agents communicate on wireless network to maximize their local and gobal utilities.

Agents with Collective Intelligence have Been Successful in other Domains

MINI-ROBOT RESEARCH — Sandia National Laboratories (Photo by Randy Montoya) Large groups of small vehicles

Diablo, Blizzard Entertainment, 1996

Entertainment computing

Lighting Mote Collectives

• • z : worldline - action/state vector of agents and environment (sensors & actuators)  : agent, ^  : other agents • z  , z ^  • The key is finding good utility functions: – G(z) : global utility that balances energy and performance multiobjective function.

– g  (z) : private utility that might take on the preferences on different room occupants.

Medical & Home Security Marisela Avalos

• High density wireless motes could detect changes in patient patterns in a manner that is less intrusive than other devices such as cameras or pressure sensors on toilets.

• Such networks could be useful for other security concerns: – Intruders – Fire or extreme temperatures – Extend network for self-reporting of injuries

Personalization in Medical Care - Avalos, Newman, Ng, Rahmani & Sandhu “A sense of community beyond that contained within the walls of

a long-term care residence is important to improving the quality of life of the confined elder. Without a community presence relieving the isolation, the culture of illness and debilitation overtakes a culture of living.”

—Susan E. Mazer, President of Healing HealthCare Systems Personal mote on keychain

THE CONCEPT

CommuniCast is an electronic broadcast display, or bulletin board, that dynamically posts events, activities, and other pertinent information in the presence of a wireless device and based on the user’s display preferences. The goal is to improve the level of communication and social interaction among the senior citizen community.

MS Project Proposal Newman

• To construct a complete product prototype integrating: • Develop system for assigning preferences to announcements that takes privacy considerations into account.

– HCI Considerations: • Display Readability and Comprehension • Unobtrusive Wearable Motes QuickTime™ and a TIFF (Uncompressed) decompressor are needed to see this picture.

– Customer System Preferences: • Ethnography Study – Manufacturing Issues: • Expect prototype to be designed for manufacturability

Display device (WLAN- & mote enabled) Mote-based keychain

System Architecture

Upon finding a user in range, display device uses user identification to request appropriate information to display for that user. This information is then displayed to the end-user.

Administrative Office

Web interface connects to server application in order to view and update information in the database.

Display device requests are forwarded to server application.

Web interface server application WLAN Server application queries and modifies database based on incoming requests from local Web interface or remote display devices.

Mote-enabled display scans for mote based keychains containing user identification over RF.

database *Slide care of Sir Jaspal Sandhu

Evaluation: Research Goal #1

• Computer simulation (McGrath) of personalized, preference-balancing decision making Model a room w/o windows in order to control the simulation, restrict attention to preference only. The room should contain multiple users sharing one switch, or bank of lights.

Specify various occupancy patterns in the space, and backtrack from preference testing data to determine how many ideal perceptions, too dark and too bright perceptions are registered under the two competing control algorithms.

Preference data was obtained though an experimental hardware simulation; in this case we are computer-simulating these preferences for the group of occupying the space

Evaluation: Research Goal #2

• Experimental simulation (McGrath) of improved target illuminance levels The goal is to quantify how the intelligent controller compares to a commercial controller in maintaining target illuminance at the worksurface In order to do so, test the commercial and intelligent systems under the same external (room) conditions, perturbing/varying the worksurface illuminance. Each system will have a target illuminance that it is trying to maintain. Therefore, by recording the deviation from target for each perturbation, a comparison of the two systems is possible.

Evaluation: Research Goal #3

• Computer simulation (McGrath) of increased E savings through demand responsiveness Model a space without natural light, in order to provide the most conservative estimate, and to control the simulation. Condition the space throughout one 24hr. weekday, assuming a typical 8hr workday. To provide an upper bound on savings, simulate a second case, in which all bodies are present throughout the entire 24hr. day.

Commercial algorithms will set one target illuminance for that whole period, while the intelligent algorithm will set varying targets depending upon the price schedule. The illuminance will determine the luminance (output) of the lights, from which we can calculate energy consumption, and cost.

Evaluation: Research Goal #4

• Laboratory experiment, sample survey (McGrath) to evaluate user satisfaction Select a test space with significant amounts of natural light throughout the day. Install a commercial daylighting system, run it for a week under variable cloud conditions and issue a survey. Install the intelligent daylighting system, run it for a week, under the same conditions, and issue a second survey. The surveys are to be complimented with bottom-line data: number of manual overrides, electricity consumption and expense, and worksurface illuminance patterns. Candidate test spaces include the BID office space (full-scale test), and LBNL’s electrochromic windows test-bed (controlled prototype test).