Team Members: Pramod Varshney, Can Isik, Chilukuri Mohan, H. Ezzat Khalifa,

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Management Of The Built Environment To Reduce
Exposure Risk

• Team Members Pramod Varshney, Can Isik,
  Chilukuri Mohan, H. Ezzat Khalifa,
• Onur Ozdemir, Ramesh Rajagopalan, Priyadip Ray,
• James Smith, Jensen Zhang

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Outline

• SAC 2005 - Main Concerns
• Problem
• Definition
• Motivation and Objectives
• Research Needs
• Integrated Components of this Task
• Optimization and control
• Indoor Sensor Networks
• Testbeds
• Summary and Future Work

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SAC 2005 - Main Concerns

• a lot of the work was premature pending
  definition of a plausible problem scenario
• an approach based on temperature or CO2 control
  might be feasible but should only be considered
  if the state of the art in indoor environmental
  controls will be advanced
• necessary to refocus this effort
• consult with practicing HVAC engineers, and
  make inquiries from professionals in the
  industry

4
The Problem Definition

• How can we improve the indoor air quality (IAQ)
  around each individual in a built environmental
  system (BES) while keeping the cost at a
  reasonable level?
• Treat built environment as a collection of
  multiple controllable zones
• Shift from one-size-fits all (OSFALL) paradigm
  and move towards have-it-your-way (HYWAY)
  paradigm.

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There is no Free Lunch!

• Improved health, productivity and comfort, at
  the expense of increased system complexity
• Additional infrastructure
• Increase in cost, computation, communication and
  actuation
• Additional burden of coupling effect between
  zones

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Motivation and Objectives

• Existing one size fits all solutions leave many
  occupants dissatisfied with their environments,
  limiting productivity and affecting health
• New research shows that higher IAQ improves
  health, learning and productivity - Dr. Ole
  Fanger
• Empowering each occupant with the ability to
  control ones own environment improves
  satisfaction and productivity expected
  technological changes will help realize this
  vision. This will require a paradigm shift in
  HVAC technology
• I predict a dramatic change in HVAC technology
  in the future - Dr. Ole Fanger

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Research Needs

• Controlling individual environments while keeping
  the cost at a reasonable level is an optimization
  problem, whose solution requires
• Measuring environmental parameters at an
  individual level with a complex network of
  sensors
• Providing control actuators at an individual
  scale but with coordination
• Reacting to changes in the system variables such
  as occupancy and weather conditions
• In order to customize the IAQ, a network of
  sensors, controllers and actuators is needed. A
  wireless network allows low-cost retrofitting of
  existing buildings

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Goal

• Implement cost effective personal control of the
  microenvironment, which enhances individual
  health, satisfaction and productivity, by
  integrating sensing, intelligent information
  processing and distributed control,.

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Integrated Components of this Task

• Improve indoor air quality by
• Optimization and control new methodologies for
  real-time control
• Indoor sensor network design, placement, data
  processing, and spatio-temporal profiling
• Test-beds design of new test-beds and
  implementation of developed methodologies on
  these test-beds

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Micro-Level Demand-Controlled Ventilation (DCV)

• Main Objective To improve IAQ around each
  individual in an office building
• Approach Develop optimization algorithms that
  will improve IAQ in every single office for each
  individual
• Constraints Energy consumption, costs and
  individual comfort

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DCV At The Micro Level

• Emissions in a room
• CO2 - surrogate for emissions by people or
  through human activity
• TVOC surrogate for emissions from room contents
  and furniture
• Our Focus CO2 levels as the IAQ criterion for
  optimization
• Goal Optimization of ventilation rates (CO2
  levels) and energy costs in a multi-zone BES
• Approach DCV at the micro-level with one
  controllable diffuser in each room (e.g.,
  Variable Air Volume VAV)
• ASHRAE Std. 62 2004 allows for ventilation
  rates based on both occupancy and floor area. A
  DCV system based only on CO2 will address the
  people component but not the passive component.

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CO2 Levels and Energy

• For a single zone
• CO2 concentration at steady state is known
• The energy model is known
• Main energy consumed by the HVAC is
  proportionally related to the cooling/heating
  coil load
• S.Atthajariyakul, T. Leephakpreeda,Real-time
  determination of optimal indoor-air condition for
  thermal comfort, air quality and efficient energy
  usage, Energy and Buildings, vol.36, pp.
  720-733, 2004

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Illustrative Example

• An office building with 25 rooms
• Intra-room uniformity One temperature and RH
  value assumed within each room(well-mixed
  conditions)
• Inter-room uniformity All rooms have identical
  DBT and RH values
• Occupancy 82 people in the building, with 1-5
  people in each room
• Goal Find optimum outside airflow rates (Fo) for
  each room, with
• Minimal energy consumption
• CO2 levels Ci lt 800 ppm threshold in each room

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Preliminary Results

• For the same total airflow rate, we compare two
  alternatives
• OSFA solution Same airflow rate (Fo) in each
  office
• HIYW solution Fo adjusted using occupancy
  information (room-level DCV)

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Preliminary Results

• Improved IAQ at the same energy cost
• Future Work Optimization based on airflow
  dynamics between rooms (Task3.2), variable indoor
  air temperatures, occupancy variations and
  variable metabolic rates

DCV at the Micro Level (HYWAY) OSFALL Solution
Power Consumption (kW) 56.6 56.6
of Occupants for whom Ci gt 800 ppm 0 56 of 82
of Rooms where Cigt800 ppm 0 13 of 25
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Paradigm Shift from OSFA to HIYW
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Integrated Components of this Task

• Improve indoor air quality by
• Optimization and control improved IAQ via new
  methodologies for real-time control
• Indoor sensor network design, placement,
  data-processing and spatio-temporal profiling
• Test-beds design of new test-beds and
  implementation of developed methodologies on
  these test-beds

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Why Distributed Large-scale Sensor Networks?

• Multiple sensors needed to acquire state
  information in each micro-environment, for
  implementation of HYWAY paradigm
• Higher resolution and fidelity data available in
  a sensor-rich environment will improve
  distributed monitoring
• Sensors need to be networked for system-wide
  optimization and real-time control of i-BES
• Wireless networks facilitate lower-cost
  retrofitting of existing buildings

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Previous Work

• N. Lin, C. Federspiel and D. Auslander,
  Multi-sensor Single-Actuator Control of HVAC
  Systems, Int. Conf. For Enhanced Building
  Operations, Richardson, TX, 2002
• Simulation results demonstrating the advantage of
  using atleast one sensor for each room compared
  to one sensor for many rooms
• Assumes a single actuator
• Wang, D. E., Arens, T. Webster, and M. Shi. "How
  the Number and Placement of Sensors Controlling
  Room Air Distribution Systems Affect Energy Use
  and Comfort." International Conference for
  Enhanced Building Operations, Richardson, TX,
  October, 2002
• Simulations results showing the benefits of using
  more than one temperature sensor to control
  conditions in the occupied zone of a room

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Previous Work (contd)

• H.Zhang, B.Krogh, J.F. Moura and
  W.Zhang,Estimation in virtual sensor-actuator
  arrays using reduced-order physical models, 43rd
  IEEE Conference on Decision and Control, December
  14-17,Atlantis, 2004
• Application of sensor networks for real-time
  estimates of the values of a distributed field at
  points where there are no sensors
• Assumes linear system models
• Clifford C. Federspiel, Estimating the Inputs of
  Gas Transport Processes in Buildings, IEEE
  Trans. on Control Systems Technology, 1997
• Estimation of the strength of a gas source in an
  enclosure
• Applies Kalman filtering for state estmation

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Multi-sensor Detection and Sensor Placement

• Application of multiple sensors for improved
  detection of indoor pollutants
• Improved detection enables reduced exposure of
  occupants to pollutants
• Development of cost-effective sensor placement
  strategies for improved indoor air quality

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Multi-sensor Detection Example
Simulated concentration profile of a gaseous
pollutant released in still air at a point in a
room of 9m x 10m x 6m dimensions with source
located at (5,5,0)
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Numerical Example

• An example demonstrating the utility of multiple
  sensors for fast detection of a pollutant. The
  simulations are for a
  room with source located at the center of the
  room

Probability of detecting whether pollutant
concentration exceeds a predefined threshold
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Sensor Placement Problem

• Goal To determine optimal locations of sensors
  for detection of gaseous pollutants in a room or
  a large hall
• Evaluation measure Improved probability of
  detection of gaseous indoor air pollutants
• Constraint Number of sensors to be deployed
• We assume a spatial probability distribution for
  the location of the source.
• Approaches
• Uniform placement where sensors are equi-spaced
  from each other
• Closest point placement where sensors are placed
  at locations closest to the mean of the spatial
  source distribution
• Intelligent placement strategies for quick
  pollutant detection and reduction of exposure
  time

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Simulation Results
Approach Detection probability
Uniform placement 0.35
Closest point placement 0.68
Intelligent placement strategy (Evolutionary algorithm with local search) 0.97

• Sensor placement results with 3 sensors placed in
  a 9x10x6m room
• Intelligent placement strategy outperforms
  intuitive strategies such as uniform placement in
  terms of the detection probability

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Spatio-temporal Profiling of Environ. Parameters

• In environmental applications, sensor networks
  monitor physical variables governed by continuous
  distributed dynamics
• Results in correlated sensor observations
• Difficulty Spatial and temporal irregularities
  in sampling
• Problem Produce real-time estimates of the
  values of a distributed field at points where
  there are no sensors
• H.Zhang, B.Krogh, J.F. Moura and
  W.Zhang,Estimation in virtual sensor-actuator
  arrays using reduced-order physical models, 43rd
  IEEE Conference on Decision and Control, December
  14-17,Atlantis, 2004.

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Our Approach

• We propose a supervised local function learning
  approach to estimate the observation of a sensor
  from a subset of its neighbors
• The goal is to estimate an unknown
  continuous-valued function in the relationship
• y g(X) n
• where, the random error/noise (n) is
  zero-mean, X is a d-dimensional vector (e.g.,
  Position coordinates of sensors) and y is a
  scalar output (e.g., concentration of CO2)

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Our Approach (contd)

• A generalized regression neural network (GRNN)
  has been used, due to its superior interpolation
  abilities and fast convergence
• GRNN learns the spatial concentration function
  from the observations provided by sensors in the
  neighborhood (circular region) of a desired
  location

The objective is the real time estimation of
concentration value at point C from the
neighboring sensors
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Simulation Results for Spatial Profiling of CO2
- 1/2

• Estimation of the CO2 levels at each
  microenvironment from sparsely distributed sensors

One realization of the spatial profile of CO2 and
location of sensors
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Simulation Results for Spatial Profiling of CO2
2/2
How many sensors are adequate ?
Summary About 70 sensors are adequate for MSE
of 0.06 additional sensors do not significantly
improve MSE
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Outcomes

• The right number of sensors can be determined
  to reduce cost
• Development of multi-criteria, system-wide
  optimization methodologies for HYWAY systems
• Multiple, interdependent, individually
  customized microenvironments controlled by
  distributed environmental control systems (e.g.,
  PVDs) will be aided by the fine-grained
  characterization of IAQ parameters

Future Work Reduced order models for building
inter-zonal transport (Task 3.2) will provide
more realistic and computationally faster models
to test our algorithms
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Experimental CO2 Data - Location of Sensors
In collaboration with Reline Technology, India
University of Technology, Sydney, Australia
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Experimental CO2 Data Concentration Levels
More details about this experimental test-bed
are provided in subsequent slides
34
Integrated Components of this Task

• Improve indoor air quality by
• Optimization and control improved IAQ via new
  methodologies for real-time control
• Indoor sensor network design,
  placement,data-processing and spatio-temporal
  profiling
• Test-beds design of new test-beds and
  implementation of developed methodologies on
  these test-beds

35
Sensor Network Testbed in Link Hall

• Goal Data collection and evaluation platform
  for various control and data-processing
  algorithms being developed
• Wireless sensor network test-bed is being set-up
  on 3rd floor of Link Hall
• Four closed spaces/rooms on the third floor of
  link hall will be monitored by the WSN
• Each closed space will have 5 ABLE ARH-T-2-I-W
  temperature RH sensors, 5 TI 4GS CO2 sensors,1
  pressure sensor and a information processing unit
  called Sensor Network Access Point (SNAP)
• In future this test-bed will be incorporated in
  the building control system for evaluation of the
  complete system

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Sensor Network Test-bed in Link Hall (contd)
Possible Locations of SNAPs
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ICUBE Lab (2007)

• Goal Allow people to adjust their own indoor
  air parameter settings within an office or a
  cubicle while maximizing overall energy usage
• Giving people (users of the lab) what they want
  for control settings without increasing baseline
  costs for the larger space
• The lab will consist of one test lab (with
  uniform settings) and one control lab (with
  individual settings)
• The lab will feature intelligent controls, sensor
  networks
• The lab will employ raised floor diffusers with
  actuated damper controls
• The lab will behave like a real building, with
  people able to adjust their own thermostat
  settings
• Multiple configurations will be possible
  classroom to office to lab

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ICUBE Lab
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Testbed Ready for Comparison of New Algorithms

• Main Goal Develop and test multiple-input
  multiple-output (MIMO) control algorithms for
  intelligent HVAC control
• DAQ system and the actuators have been installed
  on the HVAC demonstrator (Hampden H-ACD-2-CDL) in
  Link-0031
• System Characterization Experiments (full
  capability)
• Single-Input Single-Output (SISO) Control
  Experiments (full capability)
• MIMO Control Experiments with combination of CO2,
  TVOC and temperature control (technically ready,
  awaiting CO2 and TVOC sensors)

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System Characterization

• SISO Temperature Open Loop Response
• Illustration of DAQ on the HVAC demonstrator
• Enables system characterization so that control
  algorithms can be developed
• M.L. Anderson, M.R. Buehner, P.M. Young, D.C.
  Hittle, C. Anderson, J. Tu, and D. Hodgson, An
  Experimental System for Advanced Heating,
  Ventilating, and Air Conditioning (HVAC)
  Control, to appear in Energy and Buildings,
  2006

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SISO Control

• SISO Temperature Control
• Illustration of developed SISO controller on the
  HVAC demonstrator
• Future Work Develop and test MIMO controllers
  that will enable us to overcome coupling effects
  existing in todays state-of-the-art HVAC systems
  resulting in improved performance
• M.L. Anderson, M.R. Buehner, P.M. Young, D.C.
  Hittle, C. Anderson, J. Tu, and D. Hodgson,
  MIMO Robust Control for Heating, Ventilating,
  and Air Conditioning (HVAC) Systems, submitted
  to IEEE Transactions on Control Systems
  Technology, 2005

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Summary and Future Work

• Optimization and control
• Algorithms that enable us to move towards HYWAY
  paradigm without compromising comfort and energy
  costs are being developed
• More complex and realistic algorithms will be
  developed and tested on real test-beds
• Indoor sensor network
• Algorithms are being developed for improved
  multi-sensor detection, sensor placement and
  spatio-temporal profiling
• Algorithms will be tested on more realistic
  models (obtained from Task 3.2) and actual
  test-beds (Task 5)
• Test-beds
• Work is in progress on the set-up of sensor
  network test-beds
• Work is in progress on the set-up of HVAC
  test-beds

43
Conclusion

• a lot of the work was premature pending
  definition of a plausible problem scenario
• We have presented a specific problem scenario in
  terms of the OSFA versus HIYW paradigm shift
• an approach based on temperature or CO2 control
  might be feasible but should only be considered
  if the state of the art in indoor environmental
  controls will be advanced
• We are focusing on temperature and CO2 control at
  the micro-environment level (personal level )
• necessary to refocus this effort
• Significant refocusing of the effort as detailed
  in the presentation has been done
• consult with practicing HVAC engineers, and
  make inquiries from professionals in the
  industry
• We have initiated collaboration with United
  Technologies Research Center