resstock – evaluating home performance … – evaluating home performance upgrades ... res stock:...
TRANSCRIPT
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Building America
ResStock – Evaluating Home Performance Upgrades Across the U.S. Residential Building Stock
Moderator: Linh Truong– National Renewable Energy Laboratory Panelist: Eric Wilson– National Renewable Energy Laboratory
March 29, 2017
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Some Housekeeping Items
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Some Housekeeping Items (cont’d)
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If you are having difficulty viewing the materials through the webinar portal: You may find PDF copies of the presentation at the website listed here and you may follow along as our speaker presents. Today’s webinar is being recorded and the recording will be available on the DOE YouTube channel within a few weeks.
http://energy.gov/eere/buildings/building-america-meetings#current
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Agenda Welcome and Introductory Remarks
Overview of Building America (buildingamerica.gov) Linh Truong - National Renewable Energy Laboratory
Presentations Eric Wilson- National Renewable Energy Laboratory
Questions and Answers
Closing Remarks
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Buildings.Energy.gov
Building America Website: • Program information • Top Innovations • Climate-specific case studies • Building America Update newsletter • Building America Solution Center • Publications Library
Building America
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www.buildingamerica.gov
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Eric Wilson, Research Engineer, National Renewable Energy Laboratory
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Eric joined NREL in 2010. His recent activities include developing multifamily modeling capabilities for the BEopt building energy optimization software, developing an analysis framework and data visualization for national residential building stock models, and leading updates of the Building America House Simulation Protocols. Prior to joining NREL, Eric researched the energy implications of pressure drop in residential duct systems. He also performed energy audits and design assistance for a state energy program and conducted blower door tests on tribal housing across the country.
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ResStock: Evaluating Home Performance Upgrades Across the U.S. Residential Building Stock
Eric Wilson, ResStock Project/Product Lead Craig Christensen, ResStock Initial Concept & Strategic
Direction Scott Horowitz, Residential Analysis & Tools Team Lead Residential Buildings Research Group National Renewable Energy Laboratory
March 29, 2017
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Acknowledgments
ResStock development has been supported by: U.S. Department of Energy • Office of Energy Efficiency and Renewable Energy
Building Technologies Office, Residential Buildings Integration
• Office of Energy Policy and Systems Analysis (EPSA) • EERE Office of Strategic Programs U.S. Environmental Protection Agency (EPA) Region 8 Office
Region 10 Office Bonneville Power Administration (BPA)
Industry partnerships under development ResStock and ComStock leverage long-term investment in building energy modeling by DOE
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• Context & Motivation • ResStock Approach • Example Results • Looking Ahead
Outline
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Context & Motivation
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ResStock + ComStock
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ResStock + ComStock
Data-driven, physics-based simulation of the U.S. Residential and Commercial building stocks
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ResStock + ComStock
Data-driven, physics-based simulation of the U.S. Residential and Commercial building stocks
using large public and private datasets and modern computing resources
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ResStock + ComStock
Data-driven, physics-based simulation of the U.S. Residential and Commercial building stocks
using large public and private datasets and modern computing resources
to achieve unprecedented granularity in modeling building energy use and demand
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ResStock + ComStock
Data-driven, physics-based simulation of the U.S. Residential and Commercial building stocks
using large public and private datasets and modern computing resources
to achieve unprecedented granularity in modeling building energy use and demand
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Homes use 22% of primary energy in U.S.
Context & Motivation
Primary energy consumption by sector, 2014
Commercial 19%
Transportation 28%
Industrial 32%
Residential 22%
Source: U.S. Energy Information Administration, Monthly Energy Review, Table 2.1 (March 2015). Preliminary data for 2014
Note: Sum of individual percentages may not equal 100 because of independent rounding
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Homes use 37% of electricity in U.S.
Context & Motivation
Transportation 0.2%
Residential 37%
Commercial 35%
Industrial 27%
Electricity Consumption by Sector (2013)
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Homes contribute to roughly 50% of peak electric demand
Context & Motivation
Large C&I Large C&I
Small Commercial
Small Commercial
Residential
Residential
March 9, 2011 August 3, 2011
Peak Demand on Texas Grid (ERCOT)
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If just one of every 10 U.S. homes cut its energy use by 25%, Americans could save a total of more than $5 billion per year on their energy bills.
Context & Motivation
— U.S. DOE Building Technologies Office’s Multi-Year Program Plan for Fiscal Years 2016 through 2020
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How do we find the best opportunities?
Context & Motivation
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Context & Motivation High-Granularity Approach
All single-family homes in Washington and Oregon
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Context & Motivation High-Granularity Approach
Drill-and-fill applicable
All single-family homes in Washington and Oregon
1,200,000 homes
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Context & Motivation Typical Approach High-Granularity Approach
Heating fuel type Weather station
Gas Heat
Electric Heat
Segmented by:
Payback (years)
5 10+ 3
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Context & Motivation Typical Approach High-Granularity Approach
Heating fuel type Weather station
Heating fuel type Weather station Year built Home size Number of stories Foundation type Occupancy etc.
Payback (years)
5 10+ 3
Gas Heat
Electric Heat
Segmented by:
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Context & Motivation Typical Approach High-Granularity Approach
Heating fuel type Weather station
Heating fuel type Weather station Year built Home size Number of stories Foundation type Occupancy etc.
Payback (years)
5 10+ 3
Segmented by:
90,000 Homes with less than 5-year payback
270,000
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Context & Motivation
For credible estimates of housing stock energy efficiency potential, we need to avoid falling into the flaw of averages trap.
Source: Illustration by Jeff Danziger from book by Sam L. Savage
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The ResStock Approach
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Approach
Model every building
Model prototype buildings
Low Granularity
High Granularity
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Approach
Model every building
Model prototype buildings
Model 10s–100s of thousands of representative buildings
ResStock
Low Granularity
High Granularity
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Baseline Buildings
National Potential
Regional Potential
Census Data
Climate Locations
NREL supercomputer or Amazon Cloud
Baseline Buildings
Efficiency Upgrades
10s–100s of thousands of statistically representative
models
Building Characteristics
Natural-Gas Homes All-Electric Homes
OR
CA WA
CA
WA
OR
Approach
ResStock
Costs
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Approach – Data Sources
EIA Res. Energy Consumption Survey (RECS) NAHB Homebuilder Surveys IECC Historical Energy Codes Other national, regional, and local audit databases Census
Data
Climate Locations
Costs
Building Characteristics
Census American Community Survey (ACS)
EIA Electricity and fuel costs NREL OpenEI.org Utility Rate Database NREL/Navigant Measure Cost Database
NREL TMY3 weather data
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Approach – Data Sources
EIA Res. Energy Consumption Survey (RECS) NAHB Homebuilder Surveys IECC Historical Energy Codes Other national, regional, and local audit databases Census
Data
Climate Locations
Costs
Building Characteristics
Census American Community Survey (ACS)
EIA Electricity and fuel costs NREL OpenEI.org Utility Rate Database NREL/Navigant Measure Cost Database
NREL TMY3 weather data
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Approach – Conditional Probability Distributions
Mountain
Northwest
California Southwest
Southeast
Great Lakes
New England
Mid-Latitude
Mid-Atlantic
North Central
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Approach – Conditional Probability Distributions
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Approach – Conditional Probability Distributions
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Approach – Conditional Probability Distributions
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Approach – Statistical Sampling
• There exists a very large number of possible combinations of building characteristics (across different locations and vintages).
• Therefore, statistical sampling is used to
automatically generate representative models to be simulated.
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Approach – Statistical Sampling
• There exists a very large number of possible combinations of building characteristics (across different locations and vintages).
• Therefore, statistical sampling is used to
automatically generate representative models to be simulated.
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Approach – Statistical Sampling
Example building model (with highest probability characteristics)
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Approach – Statistical Sampling
Example building model (with some lower probability characteristics)
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Approach – Building Simulations
DOE Energy Modeling Ecosystem
EnergyPlus Detailed subhourly simulation engine
OpenStudio open-source platform supporting applications that use EnergyPlus
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Approach – Building Simulations
How many simulations are necessary?
Predicted source energy consumption [MBtu/yr/house]
Number of simulations
350,000 baseline simulations
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350,000
baseline simulations
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Modeled (y-axis) vs. EIA RECS (x-axis) Average Source Energy per House: 106 Btu/yr
Electricity Region Vintage Heating Fuel Type
Natural Gas
Approach – Validation/Calibration
Cumulative Distribution
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Aggregated by Region/Vintage Combinations
Before Calibration After Calibration
Modeled (y-axis) vs. EIA/RECS (x-axis) Average Electricity Consumption per House: 106 Btu/yr source
Approach – Validation/Calibration
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350,000 20 million
2.4
baseline simulations
upgrade simulations
years of computing time
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Example Results
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DOE Office of Energy Policy and Systems Analysis
Example Results: 2016 Applications
Quadrennial Energy Review 1.2 An Integrated Study of the U.S. Electricity System
DOE Building Technologies Office Home Improvement Catalyst
(HI-Cat)
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Focus: Technical and Economic Potential
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Example Results – Economic Potential (NPV > 0) Air Sealing Attic Insulation (R-49)
Replacing Oil Boilers with Ductless Heat Pumps Basement Wall Insulation (R-10)
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Electric Savings – Technical Potential
0%
10%
20%
30%
40%
50%O
RW
AC
AN
M AZ ID NV
WY
MT
UT
CO
OK
AR LA TX NC
SC FL DC VA
WV
MD
GA
DE
KY AL
TN MS
MO KS
NE
ND IA SD
MN
OH IN IL MI
WI
PA NJ
NY
MA
NH CT RI
VT
ME
Pacific Mountain WestSouth
Central
South Atlantic East SouthCentral
West North Central East NorthCentral
MiddleAtlantic
New England
Perc
enta
ge o
f sta
te's
sin
gle-
fam
ily d
etac
hed
elec
trici
ty c
onsu
mpt
ion
Upgrade ElecFurn toVSHP at wear out
DHP (displaces electricbaseboard today) (60%)
Upgrade Central ASHP toVSHP
AC Upgrades
Smart thermostat
Duct sealing
Air sealing
Attic Insulation
Foundation Wall Ins.
Drill-and-Fill Wall Ins.
Low-E Storms (DIY)
Upgrade Electric WH toHPWH
ES Appliances
LEDs
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Electric Savings – Economic Potential w/ Financing (NPV > 0)
0%
10%
20%
30%
40%
50%O
RW
AC
AN
M AZ ID NV
WY
MT
UT
CO
OK
AR LA TX NC
SC FL DC VA
WV
MD
GA
DE
KY AL
TN MS
MO KS
NE
ND IA SD
MN
OH IN IL MI
WI
PA NJ
NY
MA
NH CT RI
VT
ME
Pacific Mountain West SouthCentral
South Atlantic East SouthCentral
West North Central East NorthCentral
MiddleAtlantic
New England
Perc
enta
ge o
f sta
te's
sin
gle-
fam
ily d
etac
hed
elec
trici
ty c
onsu
mpt
ion
Upgrade ElecFurn toVSHP at wear out
DHP (displaces electricbaseboard today) (60%)
Upgrade Central ASHPto VSHP
AC Upgrades
Smart thermostat
Duct sealing
Air sealing
Attic Insulation
Foundation Wall Ins.
Drill-and-Fill Wall Ins.
Low-E Storms (DIY)
Upgrade Electric WH toHPWH
ES Appliances
LEDs
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Electric Savings – Market Potential Estimate (payback < 5 years)
0%
10%
20%
30%
40%
50%O
RW
AC
AN
M AZ ID NV
WY
MT
UT
CO
OK
AR LA TX NC
SC FL DC VA
WV
MD
GA
DE
KY AL
TN MS
MO KS
NE
ND IA SD
MN
OH IN IL MI
WI
PA NJ
NY
MA
NH CT RI
VT
ME
Pacific Mountain WestSouth
Central
South Atlantic East SouthCentral
West North Central East NorthCentral
MiddleAtlantic
New England
Perc
enta
ge o
f sta
te's
sin
gle-
fam
ily d
etac
hed
elec
trici
ty c
onsu
mpt
ion
Upgrade ElecFurn toVSHP at wear out
DHP (displaces electricbaseboard today) (60%)
Upgrade Central ASHPto VSHP
AC Upgrades
Smart thermostat
Duct sealing
Air sealing
Attic Insulation
Foundation Wall Ins.
Drill-and-Fill Wall Ins.
Low-E Storms (DIY)
Upgrade Electric WH toHPWH
ES Appliances
LEDs
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Enclosure+HVAC+WH Packages Enclosure+HVAC Packages
Enclosure Packages HVAC Packages
Package Results – Economic Potential w/ Financing (NPV > 0)
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Actionable results for states and cities
- 5,000 10,000 15,000
Per-House Electricity Savings
[kWh/yr] - 1 2 3 4
Upgrade Electric Furnace (and AC)to High-Eff. Heat Pump at wear out
Drill-and-Fill Wall Cavities
Ductless Heat Pump (displaceselectric baseboard)
LED Lighting
Smart Thermostat
Upgrade Electric WH to HPWH (80gal)
Duct Sealing & Insulating
Foundation Wall Ins.(Bsmt, Crawl)
R-49 Attic Ins.
Air Sealing
Statewide Electricity Savings
[TWh/yr]
Upgrade Electric WH to HPWH
Utility bills 1.5
billion dollars per year
Cost-effective savings for Virginia
Top 10 Upgrades
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Evaluate incentives – Drill-and-Fill Wall Insulation
7 million homes with less than 5 year payback
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Evaluate incentives – Drill-and-Fill Wall Insulation
7 million homes with less than 5 year payback
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Evaluate incentives – Drill-and-Fill Wall Insulation
7 million homes with less than 5 year payback
23 million homes with less than 5 year payback
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Looking Ahead
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Applications
EERE Building Technologies Office EERE Office of Strategic Programs Office of Energy Policy and Systems Analysis
• Quadrennial Energy Review 1.2 • Home Improvement Catalyst • Grid load modeling
• Regional Planning Tool • Low-Income EE Potential
• Demand response
• City energy strategy
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Looking Ahead: State-Specific Results
48 state fact sheets based on QER analysis
• High-level results
• Top priority upgrades
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Looking Ahead: ResStock Website
Interactive visualizations of:
• Housing characteristics
• Baseline consumption by end-use, fuel
• Savings and cost-effectiveness for retrofits
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Looking Ahead: New capabilities
Demographic parameters
low-income EE potential What is the potential for energy efficiency in low-income communities?
Which upgrades have the best Savings-to-Investment Ratio in each city, state, or customer segment?
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Looking Ahead: New capabilities
Time-of-Savings + Load Flexibility When do savings from home performance upgrades occur?
What is the potential for reducing peak demand?
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Winter peak
Looking Ahead: New capabilities
Time-of-Savings + Load Flexibility When do savings from home performance upgrades occur?
What is the potential for reducing peak demand?
Heat pump (min. eff.)
Peak demand reduction achieved by replacing an electric furnace with
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Looking Ahead: New capabilities
Winter peak
Heat pump (min. eff.)
Heat pump (max. eff.)
Heat pump (max. eff.) + Wx
Peak demand reduction achieved by replacing an electric furnace with
Time-of-Savings + Load Flexibility When do savings from home performance upgrades occur?
What is the potential for reducing peak demand?
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Looking Ahead: New capabilities
Time-of-Savings + Load Flexibility When do savings from home performance upgrades occur?
What is the potential for reducing peak demand?
Quantify the impact that time-of-use rates have on utility bills
How do home performance upgrades increase the demand response potential of smart thermostats?
What are the characteristics of homes that provide the best bang-for-the-buck in pay-for-performance programs?
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Looking Ahead: City-scale applications
City-specific data (e.g., assessors’ databases)
ResStock workflow and regional characteristics
Market engagement tools & analytics
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Thank you!
https://github.com/NREL/OpenStudio-ResStock
Electric End-Use Energy Efficiency Potential in the U.S. Single-Family Housing Stock
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Building America Newsletter
7
Visit the Meetings page at:
http://energy.gov/eere/buildings/building-america-meetings#current
Subscribe to notices about webinars and other news at: http://energy.gov/eere/buildings/subscribe-building-america-updates
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Thank You!
8
PDF copies of the presentations in this webinar are available at:
http://energy.gov/eere/buildings/building-america-meetings#current
Visit: www.buildingamerica.gov