Multifamily Energy Calculator Rapid modeling of mid-rise residential projects

Multifamily Energy CalculatorMultifamily Energy CalculatorRapid modeling of mid-rise residential Rapid modeling of mid-rise residential projectsprojects

Greg Arcangeli | Graduate Engineer | LEED AP BD+C

Cristina Woodings | Graduate Engineer

www.austinenergy.com

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● 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 GoalsHistory and Goals

Mission:“To lead the transformation

of the building industry to a sustainable future.”


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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 SavingsTrack Energy Savings


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● 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 SavingsEstimate Energy Savings


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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 Multifamily Segment ReportingReporting


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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

Prototype BuildingsPrototype Buildings


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DOE Multifamily midrise:

Floor area: 950 sq ft/dwelling unit

Window fraction: 12%

Cooling type: Packaged Terminal Heat Pump

+ Other characteristics

Prototype BuildingsPrototype Buildings


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● Multifamily energy usage intensity can vary greatly as function of unit size due to the presence of certain fixed loads (e.g. refrigerator):

o 700 sf efficiency ~ 48 kBtu/sf yr

o 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.

Prototype ShortcomingsPrototype Shortcomings


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Define a buildingprototype

Define variable matrix;Model parametrically

Store results

Optional: Add interpolation functionality

Create user interface

How to Make a Dynamic How to Make a Dynamic PrototypePrototype


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DOE’s Building America: House Simulation Protocols

Methods for scaling loads as function of dwelling unit sizee.g. Interior hard-wired lighting = 0.8*(FFA × 0.542 + 334) kWh/yr

Modeling AssumptionsModeling Assumptions


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Dwelling Unit Annual Dwelling Unit Annual ConsumptionConsumption

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.


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Dependent variable:

HVAC kWh (same for peak kW)

Independent variables:

Floor area: proxy for occupancy, area of exposed envelopeNo. bedrooms: proxy for occupancyWindow to wall ratio: envelopeRoof area: envelopeSlab area: envelopeOrientation: envelope (esp. fenestration)

Interpolation Engine: Interpolation Engine: RegressionRegression


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What else varies among otherwise typical MF projects?

Parametric PrototypeParametric Prototype


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● 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 WorkflowParametric Modeling Workflow


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● 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 WorkflowParametric Modeling Workflow


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HVAC Annual kWh

Multiple R2 = 0.992

0

2000

4000

6000

8000

10000

12000

0 2000 4000 6000 8000 10000 12000

kWh

, en

erg

y m

od

el

HVAC: Energy Model vs. HVAC: Energy Model vs. RegressionRegression


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Calculator InterfaceCalculator Interface


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Putting It All TogetherPutting It All Together


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Calculator Interface: InputsCalculator Interface: Inputs


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Calculator Interface: OutputsCalculator Interface: Outputs


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● 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 StepsNext Steps


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● 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 LearnedLessons Learned


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Contact UsContact Us

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Thank You.

Greg Arcangeli & Cristina Woodings

Austin Energy Green Building811 Barton Springs Rd. Suite 400

Austin, Texas 78704-1194

e. [email protected]

e. [email protected]

Twitter

twitter.com/aegreenbuillding

Facebook

facebook.com/aegreenbuilding


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HVAC kWh (similar for kW)

Floor area

No. bedrooms

Window/Wall

Roof area

Slab area

Orientation

Interpolation Engine: Interpolation Engine: RegressionRegression

Multifamily Energy Calculator Rapid modeling of mid-rise residential projects

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