Energy Efficient Buildings Systems Approaches & Mathematical
Challenges
2010 SIAM Annual Meeting
Pittsburgh, PA July 16, 2010
Clas A. Jacobson Chief Scientist, Controls, UTC
860.830.4151, [email protected]
With Assistance
Satish Narayanan Project Leader, Integrated
Buildings, UTRC [email protected]
860.610.7412 (office)
Igor Mezic Professor, Department of
Mechanical Engineering, UCSB [email protected]
805.893.7603 (office)
John Burns Professor, Department of
Mathematics, VT [email protected] 540.231.7667 (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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Thanks Satish 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
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•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.
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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 Picture IEA 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
aerospace systems
commercial power solutions
commercial building systems
2008 Revenue - $59 billion
Otis Pratt &
Whitney
Carrier Sikorsky
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 200820
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 200820
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
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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 Em
issi
on R
educ
tion*
Incremental Investm
ent, $B
CO2 Emission Reductions Incremental Investment to Achieve Reduction
*reflects scale up of buildings contribution to IEA Blue Map scenario, 2050
Auto Safety Regulations 2% First Cost Premium
Building Fire Safety Regulations 5% First Cost Premium
Required Building Efficiency Investments 3% Total Cost Premium 13% First Cost Premium
ECONOMIC ASSESSMENT – US ONLY
RECOMMENDATIONS Create and enforce building energy efficiency codes and labeling standards
Extend current codes and tighten over time Display energy performance labels Conduct energy inspections and audits
Incentivize energy-efficient investments Establish 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 teams Incentivize integrated team formation
Fund energy savings technology development programs Accelerate rates of efficiency improvement for energy technologies Improve building control systems to fully exploit energy saving opportunities
Develop workforce capacity for energy saving Create and prioritize training and vocational programs Develop “system integrator” profession
Mobilize for an energy-aware culture Promote behavior change and improve understanding across the sector Businesses 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 Retrofit 10-30% Reduction
Very Low Energy >50% Reduction
LEED Design 20-50% Reduction
Tulane Lavin Bernie New Orleans LA 150K ft2, 150 kWhr/m2
1513 HDD, 6910 CDD Porous Radiant Ceiling, Humidity Control Zoning, Efficient Lighting, Shading
Cityfront Sheraton Chicago IL 1.2M ft2, 300 kWhr/m2
5753 HDD, 3391 CDD VS chiller, VFD fans, VFD pumps Condensing boilers & DHW
Deutsche Post Bonn Germany 1M ft2, 75 kWhr/m2 6331 HDD, 1820 CDD No fans or Ducts Slab cooling Façade preheat Night cool
• Different types of equipment for space conditioning & ventilation
• Increasing design integration of subsystems & control
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FULL FACILITY INTEGRATION Maximize 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 design 2. 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 Challenge Complex* 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 Future David 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% efficiency to 70-80% efficiency
17 17
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 & Maintenance Concept & 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
Una
war
e
Mis
s
Loss
20%
30%
50%
Current ASHRAE 90.1
Outline
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Buildings Matter for Energy Consumption Buildings, (Complex) Systems & (Energy Performance) Gaps Mathematical Challenges
Mathematical Challenges
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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 parameters from AimDyn
from Aaborg U.
The Classroom and Office Building at UC Merced
•92000sq ft. Leed gold building A 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 Model Energy Consumption in Building
23 weakly connected subsystems identified consistent with dynamics
Graph Decomposition
Waveform Relaxation (Rapid Convergence)
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0 10 20 30 40 50 60 70-0.2
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0.6
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System Eigenvalues: 3 bands
Eigenvalues of Normalized
Graph Laplacian
WFR (3 Iter.) Complete
6 zones 68 states 85+60+30=175 (Random variables)
1 2 3 4
5
6
Uncertainty in Energy Consumption in week 100,000 DSamples (AIMDyn)
2nd Floor UC Merced
Samples
Work 1) Quasi Monte Carlo 2) 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 algorithm for dimension of state space = 10, 100, and 1000
Sharp increase in accuracy with new Sampling tool (blue) vs standard method (red)
Projection of samples on 2-D plane for DSample showing ordered structure of samples
Log # samples
1/N 1/N 1/N
MC
MC
DSample
DSample
Projection of samples on 2-D plane for Monte-Carlo (MC) showing disordered structure of samples
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
in P
aram
eter
s
0
0.2
0.4
0.6
0.8
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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
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Simulation of Room-Level Occupancy Estimation
Layout of Building 2nd floor
= Look-down
camera
Motion sensors in all rooms
Estimator Mean Error
avg per room over all time and sim runs
Mean Error avg 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
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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
System Requirements
Economic analysis
Architectural Model Building Loads
Equipment Performance
Product catalogs
Engineering performance
Synthesis Process Expert 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)
What is Hard: Products, Services and Delivery?
37
Poor operation or maintenance
Unapproachable analysis tools
As-built variances from spec
Low Energy
Mis
s
Loss
Una
war
e
Current State
Savi
ngs
Pote
ntia
l
Property Managers & Operations Staff
Operations & Maintenance Concept & Design
Contractors
Build
A & E Firms
Barrier: Scalability Climate specific Multiple subsystems Dynamic energy flows
Implication on Cost Hardware/process for calibration
Implication on Risk No Design ProCert/quality process
Barrier: Robustness Unknown sensitivities No supervisory control
Implication on Cost No ProCert process/quality process Commissioning costs/process
Implication on Risk Control of design in handoffs
Barrier: Productivity No diagnostics/guaranteed performance without consulting
Implication on Cost Measurement costs Recommissioning costs
Implication on Risk Facility operations skillsets Unbounded costs to ensure performance
UTC Proprietary
What’s Next
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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)
