Energy Simulation Tools for Buildings

1. Energy Simulation Tools for Buildings Philip Haves Lawrence Berkeley National Laboratory phaves@lbl.gov

2. Presentation Outline • Applications and motivations • History • High level architecture • Physical phenomena • Levels of modeling detail and approximations • Numerical methods and computational challenges • Users and user interfaces • Validation • Example applications: • San Francisco Federal Building – airflow network modeling, CFD • Naval Station Great Lakes – real-time simulation

3. Applications and Motivations • Building Design • Comparison of design alternatives • Code compliance • Prediction of actual energy consumption • Building Operations • Performance monitoring and fault detection • Product Development • Identify price/performance targets • Policy Development • Scenario analysis

4. History 1970 1980 1990 2000 Whole Building Systems TRNSYS HVACSIM+ SPARK IDA Modelica Post Office, NBSLD Cal-ERDA Cal-Pas DOE-2 BLAST ESP Suncode Trane Trace TAS EnergyPlus eQuest IES-VE

5. Architecture Weather Reports Envelope Simulate (time- stepping) Pre-process Systems Time Series Schedules

6. Data Model Project Material Assemblies Design Alternative 1 Other Systems 2 3 … Building Geometry HVAC Building Elements Zones Geometry Usage Location Simulation Report Water Systems Air Systems

7. Output

8. Phenomena - I Time-scales: decades, year, day, minutes, ~instantaneous Occupied Spaces • Heat and mass transfer – radiation, convection, conduction, absorption → heating, cooling and (de)humidification loads • Air flow – mechanical and natural ventilation • Pollutant transport and fate – gas, particles • Optical behavior of glazing systems and enclosures • Occupant comfort – thermal stress, draft risk, adaptive behavior • Indoor environmental quality – air pollution, odor, health, glare, noise • Occupant behavior – thermostats, windows, blinds

9. Phenomena - II Solar Radiation • Solar position, shading, atmospheric turbidity • Sky radiation: sky dome, reflections from ground and other buildings Systems • Heat and mass transfer – heating and cooling systems: • refrigeration cycles – vapor compression, absorption • heat exchangers – sensible heat transfer, condensation, evaporation • Fluid flow networks – turbulent and transitional flow • duct and piping systems • natural ventilation and infiltration • Control systems • SISO cascaded control - local loop, supervisory • optimization-based control – model predictive control

10. Levels of Modeling Detail • Stock modeling – representative buildings • Whole building - dynamic HVAC loads, quasi-static systems • simple zoning, generic systems • room-level zoning, specific equipment • Room airflow: • interzonal networks – each zone homogeneous • CFD: velocity, temperature, contaminant fields – single or connected spaces • HVAC • annual simulation: predefined flow rates, quasi steady state, manufacturers’ data • control system design, e.g. demand response: dynamic component models, explicit modeling of local loops

11. Numerical Methods - I • Wall conduction: • transfer functions – fast but no non-linearities • finite difference – explicit or implicit • typically 1-D ― 2-D or 3-D for window frames, foundations, ground • Long wave radiation • (Ti4 - Tj4) ≈ 4Tave (Ti - Tj) • View factors or area-weighted mean radiant temperature • Surface convection • qi = hc(Θ, v, L, T) (Ti - Tair) • Heat balance method – each room: 6(+) surfaces + room air → 7(+) simultaneous (linearized?) equations per room – solve for: • temperatures (floating/unconditioned) • surface temperatures and heating and cooling loads

12. Numerical Methods - II • Air flow • interzonal networks - two port non-linear elements connected at nodes that implicitly enforce mass balances • CFD: Navier Stokes equations + turbulence models: • large eddy models for airflow around buildings • simpler turbulence models for simple flows in interior spaces • Daylight distribution • daylight factors (interior/exterior illuminance) precalculated for simple geometries • ray tracing – forwards or backwards • HVAC system simulation • component models: non-linear differential and algebraic equations • solver matches inputs and outputs → well posed problem • problem reduction methods → small set of iteration variables

13. Computational Challenges • Execution speed: • CFD • ray tracing • parametric studies • optimization • Speed and robustness • system simulation: large sets of DAE’s • Parallel computing: multi-core processors, GPU’s, supercomputers • ‘embarrassingly parallel’: parametrics, some optimization methods • multi-threading: ray tracing, CFD, radiant exchange … (hand crafting) • Visualization of building performance

14. Users and Interfaces Users Interoperability/interfaces Building Information Models – object-oriented 3-D CAD Cost estimating tools Building control system • Building design engineers: • Architects • Building operators • Policy makers • Researchers • Educators

15. Cross-Validation

16. Measured/Design Ratios Relative to Design EUI Source: Frankel and Turner, NBI

17. Predicting Actual Energy Performance • Design simulations don’t model real conditions (building codes!) • occupancy schedules • plug loads • weather • Real buildings often don’t perform as expected by their designers: • faulty construction • malfunctioning equipment • incorrectly configured control systems • inappropriate operating procedures

18. Design and Controls Pre-commissioning of a Naturally Ventilated Office Tower in San Francisco using a Coupled Thermal and Airflow Simulation Program Philip Haves and Dimitri Curtil, Lawrence Berkeley National Laboratory Paul Linden and Guilherme Carrilho da Gracia, Natural Works Erin McConahey, Arup Tim Christ, Morphosis Work supported by the General Services Administration and the Federal Energy Management Program

22. Natural Ventilation Design Issues • Is buoyancy needed to supplement wind? • If so, are external chimneys needed to supplement internal buoyancy? Use coupled thermal and airflow simulation (EnergyPlus) to predict performance of different design options: • wind-driven cross-flow ventilation • wind + internal stack • wind + internal + external stack Role of simulation: • give designers and client confidence that natural ventilation will work • select system

23. San Francisco Climate • Prevailing wind from WNW • Occasional short hot periods • Daytime summertime temperatures 4-6oF lower than at airport, night-time temperatures ~equal

24. EnergyPlus Thermal: • multi-zone whole building simulation • HVAC, lighting and (day)lighting • thermal storage, 15 minute time-step • heat balances on each surface and room air • Airflow: • air-flow between spaces connected by cracks and large openings • wind pressure  pressure coefficients, velocity profile • buoyancy  space temperatures at previous time-step

25. Base temperature (oF) Wind only Internal stack Int & ext stack Int stack + wind Int & ext stack + wind 72 288 507 432 279 285 75 80 118 103 76 76 78 13 25 19 11 12 Testing the configurations: Predicted degree-hours above base temperature

26. The airflow pattern - pollutant/heat removal ability short circuit & accumulation of pollutants S E S E D E T A I L

27. Design of the flow deflector on the bottom of the lower window - second iteration.

28. Whole Building Performance Monitoring and Fault Detection • A collaboration between LBNL and United Technologies Research Center • Proof-of-concept at Naval Station Great Lakes • Real-time EnergyPlus connected to building control system • Solarimeter and sub-metering installed • Compare simulation and measurements: • Whole building electric and gas • Lighting • Plugs • Major HVAC: • Chillers • Large fans • Significant differences: • Calibrate the model? • Fix the building? 28

30. Future Work • Usability • better user interfaces to support design workflow • interoperability • Computational efficiency • Software architecture – separation of models, numerics and interfaces • Stochastic modeling – occupant behavior, weather … • Model validation • empirical validation – laboratory and real buildings • inter-model comparisons • characterization of modeling uncertainties • Process validation – standard practices, QA • protocols for different simulation goals • input data uncertainties, model uncertainties → output uncertainties • Education and training • generic principles of simulation • use of specific tools 30