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Multifamily Energy Calculator Rapid modeling of mid-rise residential projects

Multifamily Energy Calculator Rapid modeling of mid-rise residential projects. Greg Arcangeli | Graduate Engineer | LEED AP BD+C Cristina Woodings | Graduate Engineer. History and Goals. Mission: “ To lead the transformation of the building industry to a sustainable future.”.

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Multifamily Energy Calculator Rapid modeling of mid-rise residential projects

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  1. Multifamily Energy CalculatorRapid modeling of mid-rise residential projects Greg Arcangeli | Graduate Engineer |LEED AP BD+C Cristina Woodings | Graduate Engineer

  2. History and Goals Mission: “To lead the transformation of the building industry to a sustainable future.” • Austin Energy Green Building was the first comprehensive program in the US designed to encourage sustainable building. • One of our important tasks is to report the participation and effectiveness of the program.

  3. Track Energy Savings Building energy consumption savings is one of the important facets of the rating. We track predicted energy savings to measure effectiveness of program, and to make projections. • The City of Austin’s Climate Protection Goals • Generation capacity reduction for the electric utility

  4. Estimate Energy Savings • Performance: projects submit an energy model. • Prescriptive: projects do not model. A linear multiplier per square foot was derived using a prototype model similar to DOE Commercial Benchmark Models.

  5. Multifamily Segment Reporting FY 2013 • AEGB rated: 1538 units (1,744,647 sq ft) • Code permitted: 8580 units under IECC 2009 Applied multiplier example for peak demand: IECC 2009 over the baseline = 0.5 kW/unit savings FY 2013 code savings = 0.5 kW/unit X 8580 units

  6. Prototype Buildings DOE Large office: Floor area: 468,600 sq ft Aspect ratio: 1.5 Window fraction: 40% Cooling type: Water-cooled centrifugal chillers Plug and process load: 0.727 W/sf + Other characteristics

  7. Prototype Buildings DOE Multifamily midrise: Floor area: 950 sq ft/dwelling unit Window fraction: 12% Cooling type: Packaged Terminal Heat Pump + Other characteristics

  8. Prototype Shortcomings • Multifamily energy usage intensity can vary greatly as function of unit size due to the presence of certain fixed loads (e.g. refrigerator): • 700 sf efficiency ~ 48 kBtu/sf yr • 1800 sf 2-3 bedroom ~ 30 kBtu/sf yr • Works best if real projects average to prototype each year - still makes tracking less useful when comparing individual projects.

  9. How to Make a Dynamic Prototype

  10. Modeling Assumptions DOE’s Building America: House Simulation Protocols Methods for scaling loads as function of dwelling unit size e.g. Interior hard-wired lighting = 0.8*(FFA × 0.542 + 334) kWh/yr

  11. Dwelling Unit Annual Consumption

  12. Interpolation Engine: Regression Dependent variable: HVAC kWh (same for peak kW) Independent variables: Floor area: proxy for occupancy, area of exposed envelope No. bedrooms: proxy for occupancy Window to wall ratio: envelope Roof area: envelope Slab area: envelope Orientation: envelope (esp. fenestration)

  13. Parametric Prototype What else varies among otherwise typical MF projects?

  14. Parametric Modeling Workflow • Energy modeling package with scripting capability (e.g. EnergyPlus, eQUEST) • Scripting engine or GUI with integrated parametric modeling both for generating models and processing results (Open Studio, BEopt, jE+, MLE+ with MATLAB, custom) • Data repository (simplest is spreadsheet - better performance from database tools for large datasets)

  15. Parametric Modeling Workflow • Carefully choose independent variables. • Examine independent variables using statistical tools (e.g. R, Excel): significance, linearity, etc. • Examine indictors of regression model performance.

  16. HVAC: Energy Model vs. Regression

  17. Calculator Interface

  18. Putting It All Together

  19. Calculator Interface: Inputs

  20. Calculator Interface: Outputs

  21. Next Steps • Our first version--and the general concept--has been received enthusiastically by local engineering firms as a way to lower barriers to early-phase modeling. • V.2: Using same methodology, create a version with variables that explore common energy conservation measures. Integrate cost. • A powerful tool for client consultation —”live” modeling with instantaneous feedback.

  22. Lessons Learned • Plan carefully. Is this type of approach applicable to your situation? • Adding or changing variables is time intensive. Reduce up-front costs as much as possible through scripting and automated data processing. • Explicit energy model will often also be required.

  23. Contact Us Greg Arcangeli & Cristina Woodings Austin Energy Green Building 811 Barton Springs Rd. Suite 400 Austin, Texas 78704-1194 e. Greg.Arcangeli@austinenergy.com e. Cristina.Woodings@austinenergy.com Thank You. Twitter twitter.com/aegreenbuillding Facebook facebook.com/aegreenbuilding 23

  24. Interpolation Engine: Regression HVAC kWh (similar for kW) Floor area No. bedrooms Window/Wall Roof area Slab area Orientation

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