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
● 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.
Mission:
“To lead the transformation
of the building industry
to a sustainable future.”
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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
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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.
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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
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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
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Prototype Buildings
DOE Multifamily midrise:
Floor area: 950 sq ft/dwelling unit
Window fraction: 12%
Cooling type: Packaged Terminal Heat Pump
+ Other characteristics
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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):
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.
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How to Make a Dynamic Prototype
Define a building
prototype
Define variable matrix;
Model parametrically
Store results
Optional:
Add interpolation functionality
Create user interface
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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
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Dwelling Unit Annual Consumption
DHW
19%
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.
HVAC
34%
Appliances
19%
Plugs
19%
Lighting
9%
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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)
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Parametric Prototype
What else varies among otherwise typical MF projects?
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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)
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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.
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HVAC: Energy Model vs. Regression
HVAC Annual kWh
12000
kWh, energy model
10000
8000
6000
4000
2
Multiple R = 0.992
2000
0
0
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2000
4000
6000
8000
10000
12000
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Calculator Interface
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Putting It All Together
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Calculator Interface: Inputs
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Calculator Interface: Outputs
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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.
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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.
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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
Twitter
Thank You.
twitter.com/aegreenbuillding
Facebook
facebook.com/aegreenbuilding
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Interpolation Engine: Regression
HVAC kWh
(similar for kW)
Floor area
No. bedrooms
Window/Wall
Roof area
Slab area
Orientation
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