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

2 ● 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. History and Goals Mission: “To lead the transformation of the building industry to a sustainable future.”

3 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 Track Energy Savings

4 ● 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. Estimate Energy Savings

5 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 Multifamily Segment Reporting

6 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: W/sf + Other characteristics Prototype Buildings

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

8 ● Multifamily energy usage intensity can vary greatly as function of unit size due to the presence of certain fixed loads (e.g. refrigerator): o700 sf efficiency ~ 48 kBtu/sf yr o1800 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. Prototype Shortcomings

9 Define a building prototype Define variable matrix; Model parametrically Store results Create user interface Optional: Add interpolation functionality How to Make a Dynamic Prototype

10 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 × ) kWh/yr Modeling Assumptions

11 Dwelling Unit Annual Consumption Without the simulation, we already know about 70% of the consumption as a function of dwelling unit size, based on BA inputs and schedules - need to energy model to find HVAC.

12 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) Interpolation Engine: Regression

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

14 ● 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) Parametric Modeling Workflow

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

16 HVAC: Energy Model vs. Regression

17 Calculator Interface

18 Putting It All Together

19 Calculator Interface: Inputs

20 Calculator Interface: Outputs

21 ● 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. Next Steps

22 ● 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. Lessons Learned

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

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