Energy Efficient Buildings Systems Approaches & Mathematical Challenges

1. Energy Efficient BuildingsSystems Approaches & Mathematical Challenges 2010 SIAM Annual Meeting Pittsburgh, PA July 16, 2010

2. 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

3. Outline Buildings Matter for Energy Consumption Buildings, (Complex) Systems & (Energy Performance) Gaps Mathematical Challenges 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, GodfriedAugenbroe, Juan Meza, Francesco Borrelli, Jeff Borgaard, Greg Provan, Mark Myers

4. Key Points • 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 produce48% 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 Energy Breakdown by Sector Energy Intensity by Year Constructed Sources: Ryan and Nicholls 2004, USGBC, USDOE 2004

6. How Buildings Fit into the Big Picture IEA Estimates of Emissions Abatement by Source/Sector Source: IEA Energy Technology Perspective 2008

8. Sikorsky Hamilton Carrier Sundstrand $5.4 $14.9 $6.2 Fire & $6.5 Security $12.9 $12.9 Pratt & Otis Whitney UNITED TECHNOLOGIES (UTC) commercial power solutions 2008 Revenue - $59 billion aerospace systems commercial building systems 8

9. UTC SUSTAINABILITY ROADMAP Operations Advocacy Products • 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) • 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) • 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) Energy use (1997-2008) Water use (1997-2008) (gallons x 106) (revenues, $ billions) (Buts x 1012) (revenues, $ billions)

10. WBCSD EEB PROJECT A world where buildings consume zero net energy Energy efficiency first From the business voice Launch and lead sector transformation Contribution to “sustainable” buildings Communicate openly with markets

11. ECONOMIC ASSESSMENT – US ONLY Auto Safety Regulations 2% First Cost Premium Incremental Investment to Achieve Reduction CO2 Emission Reductions Required Building Efficiency Investments 3% Total Cost Premium 13% First Cost Premium Incremental Investment, $B CO2 Emission Reduction* Building Fire Safety Regulations 5% First Cost Premium *reflects scale up of buildings contribution to IEA Blue Map scenario, 2050

12. 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

13. Outline Buildings Matter for Energy Consumption Buildings, (Complex) Systems & (Energy Performance) Gaps Mathematical Challenges

14. HIGHLY EFFICIENT BUILDINGS EXIST Energy Retrofit 10-30% Reduction Very Low Energy >50% Reduction Cityfront Sheraton Chicago IL 1.2M ft2, 300 kWhr/m2 5753 HDD, 3391 CDD VS chiller, VFD fans, VFD pumpsCondensing 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 Tulane Lavin Bernie New Orleans LA 150K ft2, 150 kWhr/m2 1513 HDD, 6910 CDD Porous Radiant Ceiling, Humidity Control Zoning, Efficient Lighting, Shading LEED Design 20-50% Reduction

15. FULL FACILITY INTEGRATION Energy Recovery Ventilation Maximize natural lighting Minimize heat gain (shading) Exploit natural ventilation • Reduce energy demand early in design • 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... 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 UTC Proprietary

16. Building Systems Integration Challenge Complex* interconnections among building components From 30% efficiency to 70-80% efficiency • 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.

17. Combined Cooling, Heating & Power PureComfort™ Integrated Energy Solutions 17

18. HIGH PERFORMANCE BUILDINGS: REALITY Actual energy performance lower than predictions Design Intent: 66% (ASHRAE 90.1); Measured 44% Design Intent: 80% (ASHRAE 90.1); Measured 67% • 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 plantaffects 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.

19. ENERGY IMPACT IN DESIGN-BUILD PROCESS Concept & Design Build Operations & Maintenance A & E Firms Contractors Property Managers & Operations Staff Specialty Engineering Firms Equipment Vendors Control System Vendors Monitoring and Maintenance Companies CAD Software Vendors BIM Software Vendors Analysis Software Vendors IT Infrastructure Vendors Maintenance Software Vendors Inadequate concept exploration “We are slaves to our commissions” NZEB Unapproachable Analysis Tools “Protractors vs. daylighting simulation” Unaware 50% Design intent costed out “Value Engineering” As-built variances from spec “Can’t do it that way” Poor operation “Too complicated, I shut it off” 30% Miss Loss Current ASHRAE 90.1 Maintenance “Broken economizer” 20% 19

20. Outline Buildings Matter for Energy Consumption Buildings, (Complex) Systems & (Energy Performance) Gaps Mathematical Challenges

21. Mathematical Challenges Multiscale Modeling & Simulation Uncertainty Quantification & Risk Assessments Dynamics & Control Software

22. Design Predictions: Problem and Implications 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 Designs over-predict gains by ~20-30%

23. Challenges: Uncertainties in the Built Environment • Few parameters and interfaces can dramatically impact energy performance • Analysis computationally intractable for large no. of parameters • Must identify critical parameters from AimDyn • Disturbances in operating environment defeat design intent for conceptually sound low energy design principles • Models of dynamics critical for estimation and control from Aaborg U.

24. DOE seed project (with LBL,UTC) Energy Efficiency in a UC Merced building The Classroom and Office Building at UC Merced A small number of parameters affect energy output! • 92000sq ft. Leed gold building

25. WFR (3 Iter.) Complete Energy Consumption in Building Thermal Network Model 23 weakly connected subsystems identified consistent with dynamics Graph Decomposition & Waveform Relaxation Graph Decomposition Eigenvalues of Normalized Graph Laplacian Nonautonomous ODE System System Eigenvalues: 3 bands Waveform Relaxation (Rapid Convergence) • 1 floor, 6 Zones 68 states, ~200 uncertain parameters • Building level, 20-30 Zone, over 200-300 states, ~400 uncertain parameters

26. 1 2 3 4 5 6 Uncertainty in Energy Consumption in week 100,000 DSamples (AIMDyn) 6 zones 68 states 85+60+30=175 (Random variables) 4% Variation 2nd Floor UC Merced • Work • Quasi Monte Carlo • Iterative Methods Samples

27. DSample: Deterministic Test Vectors for Accurate Sampling DSample Projection of samples on 2-D plane for DSample showing ordered structure of samples 1/N 1/N 1/N MC Log # samples Projection of samples on 2-D plane for Monte-Carlo (MC) showing disordered structure of samples Convergence of the new sampling algorithm for dimension of state space = 10, 100, and 1000 MC DSample Sharp increase in accuracy with new Sampling tool (blue)vsstandard method (red) -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.

28. Multi-Scale Dynamics: Problems Building, floor, room scales are coupled 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) Modes can shift dramatically during operation “optimal design intent” undesirable modes appearing in operation Figures from UTRC; Transsolar, Aalborg (Nielsen)

29. Graph Decompositions Reveal Couplings Layered system decomposition Forward and feedback pathways revealed Strong and weak interdependencies detected. -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.

30. Lagrangian Coherent Structures in N-Dimensional Systems Recently, use of ridges in finite-time Lyapunov exponent fields to identify quasi-invariant, LCS in higher dimensions was shown Lekien, Shadden, Marsden, preprint, 2006 Ridges in 3D scalar fields LCS: Dynamics of Coupling Extraction of Invariant Structures Surana, Hariharan, Narayanan, Banaszuk, ACC, 2008 Extend for Optimal Control

31. State-of-the-Art in Evacuation Dynamics Modeling 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) UTRC ABM validation with fire drill experimental data* Fire alarm • 100+ occupants undertaking unannounced fire drill in 2-storey building • 3 available exits Simulations *Lin et al. “Agent-based Simulation and Reduced-Order Modeling of Evacuation: An Office Building Case Study” PED2008 paper

32. Simulation of Room-Level Occupancy Estimation Look-down camera = Layout of Building 2nd floor Motion sensors in all rooms

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

34. Software: Problems • 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 System Requirements Architectural Model Building Loads existing Synthesis Process Expert systems System Analysis with Building Dynamics Building Control Automation Equipment Performance Zone schedules Equipment setpoints gap Product catalogs Engineering performance existing Economic analysis

35. Challenges: Scale of Computations Whole Building Simulations (complex geometry, multiple sub-systems, and realistic indoor/external uncertainties) Multi-zone Building Simulation (simplified geometry and boundary conditions) from LBNL Isolated Thermal Environment in an Individual Zone/Room from Transolar Engineering from UTRC/VT 10 TFlops → 10 Pflops Computation Capability* Complexity (scale, dynamics, nonlinearity, uncertainty…) * Assuming less than 1hour turnaround for practical design calculations

36. Challenges: Whole Building Design Tools and Software from Wetter (LBNL) from Wetter (LBNL) • 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

37. Hand off Hand off What is Hard: Products, Services and Delivery? Concept & Design Build Operations & Maintenance A & E Firms Contractors Property Managers & Operations Staff Unapproachableanalysis tools Low Energy Unaware As-built variances from spec Savings Potential Miss Poor operationor maintenance Loss Current State • 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 • Barrier: Scalability • Climate specific • Multiple subsystems • Dynamic energy flows • Implication on Cost • Hardware/process for calibration • Implication on Risk • No Design ProCert/quality process UTC Proprietary

38. What’s Next 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)