Energy Efficient BuildingsSystems Approaches & Mathematical

Challenges

2010 SIAM Annual MeetingPittsburgh, PAJuly 16, 2010

Clas A. JacobsonChief Scientist, Controls, UTC

860.830.4151, JacobsCA@utrc.utc.com

With Assistance

Satish NarayananProject Leader, Integrated

Buildings, UTRCNarayaS@utrc.utc.com

860.610.7412 (office)

Igor MezicProfessor, Department of

Mechanical Engineering, UCSBmezic@engineering.ucsb.edu

805.893.7603 (office)

John BurnsProfessor, Department of

Mathematics, VTjaburns@math.vt.edu540.231.7667 (office)

Vision

We need to do more transformational research at DOE … including

Computer design tools for commercial and residential buildings that enable reductions in energy consumption of up to 80 percent with investments that will pay for themselves in less than 10 (15) years

Dr. Steve Chu, March 5, 2009 (June 4, 2009)

We will nurture a system integration approach to building design, aided by computer tools with embedded energy analysis. It was the system integration of the automobile engine, transmission, brakes and battery that enabled Toyota to create the Prius. With computer control of ignition timing and fuel mix, today’s automobile engines operate at 20 percent higher efficiency. With computer monitoring and continuous, real-time control of HVAC systems, lighting, and shading, far more spectacular efficiencies can be realized in buildings. There is a growing realization that we should be able to build buildings that will decrease energy use by 80 percent with investments that will pay for themselves in less than 15 years. Buildings consume 40 percent of the energy in the U.S., so that energy efficient buildings can decrease our carbon emissions by one third.

Secretary Chu, Caltech Commencement, June 12, 2009

Outline

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ThanksSatish Narayanan, Igor Mezic, John Burns, Karl Astrom, John Cassidy, Michael McQuade, Richard Murray, Manfred Morari, Alberto Sangiovanni-Vincentelli, Amit Surana, Michael Dellnitz, Alberto Ferrari, Andrzej Banaszuk, Kevin Otto, Michael Holtz, Don Frey, Michael Wetter, Hubertus Tummescheit, Godfried Augenbroe, Juan Meza, Francesco Borrelli, Jeff Borgaard, Greg Provan, Mark Myers

Buildings Matter for Energy Consumption

Buildings, (Complex) Systems & (Energy Performance) Gaps

Mathematical Challenges

Key Points

4

• Energy efficient buildings. Achieving >50% over current standards (ASHRAE 90.1) is possible; proof points occur for all sizes and climates; buildings designed using climate responsive design principles.

• Market conditions – currently driven by labeling and increasingly by regulatory pressures (carbon cost not sufficient to drive market: findings through UTC led WBCSD study).

• What is hard? Delivery process handoffs are a problem and are where there is a loss of potential for energy savings in design, construction and operation.

• What are R&D areas?• Address Productivity – need design tools (configuration exploration, specification of

equipment and controls, automated implementation) – for automation on all parts of delivery chain.

• Address Robustness. Need calibrated models (experimental facilities) and ability to calculate, track and manipulate uncertainty (DFSS).

• Address Scalability. Operations – need to understand sensing requirements, failure modes and FDIA.

5

Building Energy Demand Challenge Buildings consume • 39% of total U.S. energy• 71% of U.S. electricity • 54% of U.S. natural gas

Building produce 48% of U.S. Carbon emissions

Commercial building annual energy bill: $120 billion

The only energy end-use sector showing growth in energy intensity• 17% growth 1985 - 2000• 1.7% growth projected through 2025

Sources: Ryan and Nicholls 2004, USGBC, USDOE 2004

Energy Intensity by Year Constructed Energy Breakdown by Sector

How Buildings Fit into the Big PictureIEA Estimates of Emissions Abatement by Source/Sector

Source: IEA Energy Technology Perspective 2008

Sector 2050 BAU 2050 Blue MAP Reduction

Power generation -- -- 18.2

Industry 23.2 5.2 9.1

Buildings 20.1 3.1 8.2

Transport 18 5.5 12.5

Total 62 14 48

8

aerospacesystemsaerospacesystems

commercial powersolutionscommercial powersolutions

commercial buildingsystemscommercial buildingsystems

2008 Revenue - $59 billion

Otis Pratt &

Whitney

CarrierSikorsky

Fire &

Security

Hamilton

Sundstrand

$12.9

$14.9

$12.9

$6.2

$6.5

$5.4

UNITED TECHNOLOGIES (UTC)

Operations Products Advocacy

UTC SUSTAINABILITY ROADMAP

24

26

28

30

32

34

36

38

40

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

20

25

30

35

40

45

50

55

60(Buts x 1012) (revenues, $ billions)

0

1,000

2,000

3,000

4,000

5,000

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

20

28

36

44

52

60(revenues, $ billions)(gallons x 106)

• U.S. Green Building Council (1993)

• Pew Center on Global Climate Change (1998)

• Dow Jones Sustainability Index (1999-2009)

• Global 100 Most Sustainable Corporations in the World. (2005-2009)

• World Business Council for Sustainable Development’s Energy Efficiency in Buildings project (2006-2009)

• UTC establishes first set of EH&S goals (1991)

• Otis opens TEDA facility, the world's first green elevator factory, in China (2007)

• Pratt & Whitney breaks ground on an engine overhaul facility, targeted to meet LEED platinum standards, in Shanghai (2007)

• UTC launches 2010 EH&S goals, which include absolute metrics and a new goal on greenhouse gas emissions (2007)

• Carrier introduces Evergreen® chiller (1996)

• Otis launches the Gen2TM elevator system (2000)

• UTC launches the PureComfort® cooling, heating and power system (2003)

• Pratt & Whitney launches EcoPower® engine wash (2004)

• UTC launches the PureCycle® geothermal power system (2007)

• UTC Power introduces 400 kW PureCell® system (2008)

• Pratt & Whitney flight tests PurePowerTM PW1000G engine with Geared Turbofan technology (2008)

Energy use (1997-2008)

Water use (1997-2008)

WBCSD EEB PROJECT

Energy efficiency first

From the business voice

Launch and lead sector transformation

Contribution to “sustainable” buildings

Communicate openly with markets

A world where buildings consume zero net energy

11

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

< 5 year payback <10 year payback > 10 year payback

$0

$25

$50

$75

$100

$125

$150

$175

$200

CO

2 E

mis

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n R

ed

uct

ion

*

Incre

me

nta

l Inve

stme

nt, $

B

CO2 Emission Reductions Incremental Investment to Achieve Reduction

*reflects scale up of buildings contribution to IEA Blue Map scenario, 2050

Auto Safety Regulations2% First Cost Premium

Building Fire Safety Regulations5% First Cost Premium

Required BuildingEfficiency Investments3% Total Cost Premium13% First Cost Premium

ECONOMIC ASSESSMENT – US ONLY

RECOMMENDATIONS Create and enforce building energy efficiency codes and labeling standards

Extend current codes and tighten over timeDisplay energy performance labelsConduct energy inspections and audits

Incentivize energy-efficient investmentsEstablish tax incentives, subsidies and creative financial models to lower first-cost hurdles

Encourage integrated design approaches and innovations Improve contractual terms to promote integrated design teamsIncentivize integrated team formation

Fund energy savings technology development programsAccelerate rates of efficiency improvement for energy technologiesImprove building control systems to fully exploit energy saving opportunities

Develop workforce capacity for energy savingCreate and prioritize training and vocational programsDevelop “system integrator” profession

Mobilize for an energy-aware culturePromote behavior change and improve understanding across the sectorBusinesses and governments lead by acting on their building portfolios

Outline

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Buildings Matter for Energy Consumption

Buildings, (Complex) Systems & (Energy Performance) Gaps

Mathematical Challenges

HIGHLY EFFICIENT BUILDINGS EXIST

Energy Retrofit10-30% Reduction

Very Low Energy>50% Reduction

LEED Design20-50% Reduction

Tulane Lavin BernieNew Orleans LA150K ft2, 150 kWhr/m2

1513 HDD, 6910 CDDPorous Radiant Ceiling, Humidity Control Zoning, Efficient Lighting, Shading

Cityfront SheratonChicago IL1.2M ft2, 300 kWhr/m2

5753 HDD, 3391 CDDVS chiller, VFD fans, VFD pumpsCondensing boilers & DHW

Deutsche PostBonn Germany1M ft2, 75 kWhr/m2

6331 HDD, 1820 CDDNo fans or DuctsSlab coolingFaçade preheat Night cool

• Different types of equipment for space conditioning & ventilation

• Increasing design integration of subsystems & control

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FULL FACILITY INTEGRATIONMaximize natural lighting Minimize heat gain (shading)

Exploit natural ventilation

Energy Recovery Ventilation

Optimally Sized Cooling Systems

25% primary energy footprint reduction at less than 7% incremental cost

Payback 4.2 years total / 2.5 years new items

1. Reduce energy demand early in design2. Select systems to meet demand and optimize

Efficiency gains are not additive but coupled: integrated design of siting increases daylighting which reduces lighting which reduces HVAC load...

UTC Proprietary

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Building Systems Integration ChallengeComplex* interconnections among building components

• Components do not have mathematically similar structures and involve different scales in time or space;

• The number of components are large/enormous• Components are connected in several ways, most often

nonlinearly and/or via a network. Local and system wide phenomena depend on each other in complicated ways

• Overall system behavior can be difficult to predict from behavior of individual components. Overall system behavior may evolve qualitatively differently, displaying great sensitivity to small perturbations at any stage

* APPLIED MATHEMATICS AT THE U.S. DEPARTMENT OF ENERGY: Past, Present and a View to the FutureDavid L. Brown, John Bell, Donald Estep, William Gropp, Bruce Hendrickson, Sallie Keller-McNulty, David Keyes, J. Tinsley Oden and Linda Petzold, DOE Report, LLNL-TR-401536, May 2008.

From 30% efficiencyto 70-80% efficiency

1717

PureComfort™ Integrated Energy Solutions

Combined Cooling, Heating & Power

HIGH PERFORMANCE BUILDINGS: REALITY

Design Intent: 66% (ASHRAE 90.1); Measured 44%

Design Intent: 80% (ASHRAE 90.1); Measured 67%

Actual energy performance

lower than predictions

Failure Modes Arising from Detrimental Sub-system Interactions

• Changes made to envelope to improve structural integrity diminished integrity of thermal envelope

• Adverse system effects due to coupling of modified sub-systems: • changes in orientation and increased glass on façade

affects solar heat gain• indoor spaces relocated relative to cooling plant

affects distribution system energy• Lack of visibility of equipment status/operation, large uncertainty

in loads leads to excess energy use

Source: Lessons Learned from Case Studies of Six High-Performance Buildings, P. Torcellini, S. Pless, M. Deru, B. Griffith, N. Long, R. Judkoff, 2006, NREL Technical Report.

ENERGY IMPACT IN DESIGN-BUILD PROCESS

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Contractors

Specialty Engineering Firms

Property Managers & Operations Staff

Equipment Vendors

Monitoring and Maintenance Companies

BIM Software Vendors

Control System Vendors

Maintenance Software Vendors

IT Infrastructure Vendors

CAD Software Vendors

Operations & MaintenanceConcept & Design Build

Analysis Software Vendors

A & E Firms

Inadequate concept exploration“We are slaves to our commissions”

Design intent costed out“Value Engineering”

Poor operation“Too complicated, I shut it off”

Unapproachable Analysis Tools“Protractors vs. daylighting simulation”

As-built variances from spec“Can’t do it that way”

Maintenance“Broken economizer”

NZEB

Un

awar

e

Mis

s

Lo

ss

20%

30%

50%

Current ASHRAE 90.1

Outline

20

Buildings Matter for Energy Consumption

Buildings, (Complex) Systems & (Energy Performance) Gaps

Mathematical Challenges

Mathematical Challenges

21

Multiscale Modeling & Simulation

Uncertainty Quantification & Risk Assessments

Dynamics & Control

Software

Design Predictions: Problem and Implications

Designs over-predict gains by ~20-30%

Large Variability in Performance Predictions

Performance simulations conducted for peak conditions

As-built specifications can be quite different from design intent (no performance metrics tracking)

Failure Modes From Uncertainties & Detrimental Sub-system Interactions

Changes made to envelope to improve structural integrity diminished thermal envelope (“retainer wall as a fin”)

Lack of visibility of equipment status/operation and uncertainty in loads (plug, occupancy, leaks), led to excess energy use

Challenges: Uncertainties in the Built Environment

• Disturbances in operating environment defeat design intent for conceptually sound low energy design principles

• Models of dynamics critical for estimation and control

• Few parameters and interfaces can dramatically impact energy performance

• Analysis computationally intractable for large no. of parameters

• Must identify critical parametersfrom AimDyn

from Aaborg U.

The Classroom and Office Building at UC Merced

• 92000sq ft. Leed gold buildingA small number of parameters affect energy output!

DOE seed project (with LBL,UTC)

Energy Efficiency in a UC Merced building

Graph Decomposition & Waveform Relaxation

• 1 floor, 6 Zones 68 states, ~200 uncertain parameters

• Building level, 20-30 Zone, over 200-300 states, ~400 uncertain parameters

Nonautonomous ODE System

Thermal Network ModelEnergy Consumption in Building

23 weakly connected subsystemsidentified consistent with dynamics

Graph Decomposition

Waveform Relaxation(Rapid Convergence)

...2

2)(

22)1(

2

)1(2

1)(

111

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)1(1

ii

ki

kk

ik

ii

kk

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QyQydt

dy

Tosur Tisur Tamb Tzone

C

1/hiA 1/hoA R Qsurfi Qsurfo

C

Qstructure

Rwin

)(

)()(

tQ

tQBTtA

dt

dT

internal

ext

0 10 20 30 40 50 60 70-0.2

0

0.2

0.4

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0.8

1

1.2

1.4

0 10 20 30 40 50 60 70-0.06

-0.05

-0.04

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-0.02

-0.01

0

System Eigenvalues:3 bands

Eigenvalues ofNormalized

Graph Laplacian

WFR (3 Iter.)Complete

6 zones68 states85+60+30=175 (Random variables)

1 2 3

4

5

6

Uncertainty in Energy Consumption in week100,000 DSamples (AIMDyn)

2nd Floor UC Merced

Samples

Work 1) Quasi Monte Carlo2) Iterative Methods

4% Variation

DSample: Deterministic Test Vectors for Accurate Sampling

-DSample algorithm automatically produces test vectors (e.g. for model checking), beating the curse of dimensionality while not inducing the curse of time-complexity.Example of use: for accuracy of 10-6 existing methods require 1012 samples while the new, Dynamical Systems based method requires a million samples.

Convergence of the new sampling algorithmfor dimension of state space = 10, 100, and 1000

Sharp increase in accuracy with new Sampling tool (blue) vs standard method (red)

Projection ofsamples on 2-D planefor DSampleshowing ordered structure ofsamples

Log # samples

1/N 1/N 1/N

MC

MC

DSample

DSample

Projection ofsamples on 2-D planefor Monte-Carlo(MC)showing disordered structure ofsamples

Multi-Scale Dynamics: Problems

Low energy buildings rely on intrinsic/natural dynamics

Dynamics have wide range of overlapping length/time scales

Dynamics “fragile” (to solar heat gains, occupancy) with variability in performance (energy consumption & comfort)

undesirable modes appearing in operation

“optimal design intent”

Building, floor, room scales are coupled

Modes can shift dramatically

during operation

Figures from UTRC; Transsolar, Aalborg (Nielsen)

29

Graph Decompositions Reveal Couplings

-Automatically finds system-scale forward and feedback structure in complex systems with 1000’s of variables

Example of use: supplier change requests a small change in communication protocol linking two software components. What are the possible negative consequences for system performance? Which other components will be affected?

-The set of analysis tools we are proposing to develop produces a layered decomposition, that enables efficient system-scale analysis.

Layered system decomposition Forward and feedback pathways revealed

Strong and weak interdependencies detected.

1 2 3 4 5 6 7 8

1

2

3

4

Output States

Unc

erta

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aram

eter

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LCS: Dynamics of Coupling

Surana, Hariharan, Narayanan, Banaszuk, ACC, 2008

Extraction of Invariant Structures

Lagrangian Coherent Structures in N-Dimensional Systems

Ridges in 3D scalar fields

Lekien, Shadden, Marsden, preprint, 2006

Recently, use of ridges in finite-time Lyapunov exponent fields to identify quasi-invariant, LCS in higher dimensions was shown

Extend for Optimal Control

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State-of-the-Art in Evacuation Dynamics Modeling

UTRC ABM validation with fire drill experimental data*

Agent-Based Simulations

Trajectories of individuals on fine grid simulated using parameters associated with speed and behavior

Unsuitable for real-time applications or optimization in large-scale buildings (need estimates in secs for response)

Fire alarm

Simulations

*Lin et al. “Agent-based Simulation and Reduced-Order Modeling of Evacuation: An Office Building Case Study” PED2008 paper

• 100+ occupants undertaking unannounced fire drill in 2-storey building

• 3 available exits

32

Simulation of Room-Level Occupancy Estimation

Layout of Building 2nd floor

= Look-down

camera

Motion sensors in all rooms

EstimatorMean Error

avg per room over all time and sim runs

Mean Erroravg per zone over

all time and sim runs

Sensor only (3 cameras) 0.35 4.9

EKF w/ KM, 3 cameras 0.14 1.1

EKF w/ KM, 3 cameras, motion sensors in each office/conference room

0.08 0.9

33

Concluding Remarks

• Reduced-order models of evacuation in combination with data can provide substantially higher accuracy and robust estimates of occupancy for real-time use

• Computational complexity scales with Nsensors3, not with Npeople, Nrooms, Building size

• Even use of relatively inexpensive and inaccurate sensors (e.g. motion sensors) when used with traffic models can be effective (40% estimation error reduction)

• Challenge is to enable information exchange among disparate systems cost effectively: fire/safety, security and lighting

• Other advances made leading to estimation performance improvement:– Utilizing constraints (such as door/exit width) in estimate variance computation– Use of people flow as state variable (eliminate bias error)– Projection of EKF estimate onto the feasible space (0 occupancy room size, and people

flow max flow), enforcing constraints such as positivity of state variable and conservation of number of people in building

• Need approach for occupancy estimate at time of fire alarm (initial conditions)– Estimator that uses a model of traffic during normal building operations

Software: Problems

Case Results – CHP Capacity – Sized at site 1 building baseload

344 kW

173 Tons

1,660 MBH

CHP equipment specification 344 kWe, 100% utilized 173 RT chiller, 35% utilized 1,700 MBH, 65% utilizedSavings ~ $200k/year

Peak/demand reduction

System Requirements

Economic analysis

Architectural ModelBuilding Loads

Equipment Performance

Product catalogs

Engineering performance

Synthesis ProcessExpert systems

Building Control Automation

Zone schedules

Equipment setpoints

System Analysis with Building Dynamics

gap

existing

existing

Current software and computational infrastructure• Cannot address physics of low energy buildings (natural

ventilation)• Cannot address dynamics and controls• Cannot address design (optimization, scalability)• Cannot address embedded systems validation & verification

Challenges: Scale of Computations

Isolated Thermal Environment in an Individual Zone/Room

Multi-zone Building Simulation (simplified geometry and boundary conditions)

Whole Building Simulations (complex geometry, multiple sub-systems, and realistic indoor/external uncertainties)

from UTRC/VT

from Transolar Engineering

from LBNL

Complexity (scale, dynamics, nonlinearity, uncertainty…)

10 TFlops → 10 Pflops Computation Capability*

* Assuming less than 1hour turnaround for practical design calculations

Challenges: Whole Building Design Tools and Software

• Models of physical systems and numerical solvers inseparable

• Reliance on computationally intensive simulations; analysis is difficult if not infeasible

• Models cannot be re-used; poor inheritance properties

• Mapping between design and final implementation (e.g. controls software) ad hoc and manual

• Comprehensive model libraries• Robust and efficient numerical solvers• Representation of control sequences

and auto-code generation tools• Efficient co-simulation techniques• Software for toolchain linking

Ref. “Modular Modeling and Simulation for Building Systems”, M. Wetter (LBNL), Apr. 2010

from Wetter (LBNL)

from Wetter (LBNL)

Hand

offHand

off

What is Hard: Products, Services and Delivery?

37

Poor operationor maintenance

Unapproachableanalysis tools

As-built variances from spec

Low Energy

Mis

s

Loss

Un

awar

e

Current State

Sav

ing

s P

ote

nti

al

Property Managers & Operations Staff

Operations & Maintenance

Concept & Design

Contractors

Build

A & E Firms

Barrier: ScalabilityClimate specificMultiple subsystemsDynamic energy flowsImplication on CostHardware/process for calibrationImplication on RiskNo Design ProCert/quality process

Barrier: RobustnessUnknown sensitivitiesNo supervisory controlImplication on CostNo ProCert process/quality process Commissioning costs/processImplication on RiskControl of design in handoffs

Barrier: ProductivityNo diagnostics/guaranteed performance without consultingImplication on CostMeasurement costsRecommissioning costsImplication on RiskFacility operations skillsetsUnbounded costs to ensure performanceUTC Proprietary

What’s Next

38

Look at Wiki – more material on computational needs for building design & operations

http://www.engineering.ucsb.edu/~mgroup/wiki/index.php/Computational_Science_for_Building_Energy_Efficiency_Meeting_July_8-9_2010

Peterson Institute: WBCSD Briefing

http://www.piie.com/events/event_detail.cfm?EventID=126&Media

We need to do more transformational research at DOE … including

Computer design tools for commercial and residential buildings that enable reductions in energy consumption of up to 80 percent with investments that will pay for themselves in less than 10 (15) years

Dr. Steve Chu, March 5, 2009 (June 4, 2009)