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Energy Savings Potential Estimates Using CBECS and CEUS. Michael MacDonald Oak Ridge National Laboratory [email protected] ASHRAE SLC Annual Meeting, 6-25-08. What will be presented. Brief info about CBECS and CEUS

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energy savings potential estimates using cbecs and ceus

Energy Savings Potential Estimates Using CBECS and CEUS

Michael MacDonald

Oak Ridge National Laboratory

[email protected]

ASHRAE SLC Annual Meeting, 6-25-08

what will be presented
What will be presented
  • Brief info about CBECS and CEUS
  • Brief info on building energy performance scoring using multivariate normalization
  • Brief coverage of sectoral modeling
  • Brief info on preliminary sector-wide multivariate normalization models for US and CA using CBECS and CEUS
  • First-ever preliminary results on use of such models for estimating nationwide and CA energy savings potentials based on performance levels
ceus commercial energy use survey ca
CEUS, Commercial Energy Use Survey (CA)
  • In 1996, new law led to first CEUS being conducted, with latest survey in 2003, about 60 building types, about 80% of sector covered
  • Very extensive data, used for complicated analyses, including calibrated simulations of entire commercial sector or subsectors
  • Used to develop estimates of statewide floor stock, energy intensities, and energy usage by building type
  • Building / site weights used to scale up to entire subsectors, and then results can be extrapolated to state levels
  • 2003 data currently being studied to examine building energy performance system options for CA
  • CA est: ~~700,000 buildings, 6 billion sq ft in 2003
cbecs commercial buildings energy consumption survey
CBECS, Commercial Buildings Energy Consumption Survey
  • National survey conducted periodically since 1979, latest is 2003
  • 2003 CBECS identifies about 50 commercial building types
  • Ignores buildings less than 1,000 sq ft after the original 1979 NBECS survey
  • Masks buildings > 1,000,000 sq ft
  • Has complicated survey weights that allow extrapolation to entire country
  • ~~71 billion sq ft, almost 5 million buildings in 2003
cbecs and ceus data are already used for savings potential estimates
CBECS and CEUS Data are already used for savings potential estimates
  • CBECS data provide some of the basis for the National Energy Modeling System (NEMS)
  • CEUS data used for modeling of savings potentials
  • Results available based primarily on economic-engineering models
  • Results presented here are based on performance rating models
energy performance methods
Energy Performance Methods
  • Meaningful standard of comparison?
    • Compare to what?
    • Data sources?
    • Comparison method (STD 105-2007)
  • Normalization options ... past … internal …
    • Slice-and-dice by specific characteristics
    • Additional normalization, e.g., weather
    • Simultaneous multivariate normalization
ashrae handbook fundamentals
ASHRAE Handbook, Fundamentals
  • Chapter 32 – 2005, Energy Estimating and Modeling Methods
  • Table 10, Capabilities of … Modeling Methods (p 32.31)
  • 10+ modeling methods mentioned
  • Multivariate linear regression is the one that allows simultaneous, multivariate normalization tools to be developed [simple (sometimes), fast, medium accuracy (again, compared to what?)]
economic engineering models
Economic-Engineering Models
  • Economic-engineering (E-E) models such as in NEMS use engineering data and analysis results to feed into and partially interact with an economic model of energy and investment
    • Because change is often slow, this approach often works fine for certain types of forecasting
    • But many types of energy improvements cannot be modeled reasonably, let alone well, with these models, and watch out if changes are fast
  • To forecast total energy use, normalization of energy is not required, as normalized energy is not the desired output, but normalized energy can account for total energy performance, including operational efficiency
  • New energy technologies, and impacts of those technologies on new buildings, are ably modeled in E-E tools at times, but improvements in operations are typically not
  • Operational improvements are thus typically ignored
simultaneous multivariate normalization compares performance
Simultaneous Multivariate Normalization Compares Performance
  • Tools like the Energy Star buildings rating system have been found capable of normalizing about 90% of the variation in energy use between buildings, leaving the last 10% as the basis for performance rating differences
  • This approach accounts for total energy performance, including operations (other factors such as IAQ typically handled separately)
  • The resulting performance score or rank gives a specific number on building energy performance, but not why
  • Engineering calculation tools like Energy Plus, DOE-2, etc, typically cannot say anything about how well a building performs compared to others, but can indicate why
  • Quantification of total energy performance is important, and this presentation will show the types of information possible using sectoral-wide models as opposed to building type models
building type models
Building-Type Models
  • Tools like Energy Star multivariate normalization tools are important for providing performance ratings that can be compared for specific building types
    • But coverage is limited
    • Model basis is national-average-driven
    • Keep in mind that these tools allow savings potential for a building (type) to be calculated based on score
  • Analysis for CA has indicated that state-level tools may be critical in some cases for rating building energy performance
    • Energy Star multivariate tools may cover 60% of the floor area but a much smaller percentage of all buildings in CA
    • Ratings of CA buildings using the national models appear to lead to fairly high rankings for some building types, indicating tougher normalization may be desirable in CA
sector wide models
Sector-Wide Models
  • Sector-wide models can cover almost all buildings and types
  • Performance rating will not be as robust as for building-type models, but sectoral coverage is essentially achieved
  • Savings potential is no longer limited to a building (type) but can now be calculated for the entire sector and possibly subsectors
or other types of models
Or Other Types of Models . . .
  • Entire sectors can be modeled, e.g., Buildings, Industry, Transportation
  • Scoring can be put on a curve to “grade” the entities analyzed
  • Normalization at one point in time can serve as a baseline to measure future improvements against
cbecs national model form
CBECS National Model Form
  • Energy use index (EUI) as a function of other parameters
  • EUI itself accounts for 65% of variation in energy use
  • CBECS 2003 weights used
  • Some data screening needed to remove problem facility types and include desirable parameters
  • Effective R-square = 0.85, F = 141
basic cbecs model parameters
Basic CBECS Model Parameters
  • Heating and cooling degree-days
  • Seating density for eating meals
  • Hours of operation per week
  • Personal computer density
  • Worker density
california ceus model form
California CEUS Model Form
  • Ln(energy) as a function of other parameters, with Ln(SqFt) as a parameter (not EUI-based, heteroskedasticity would not let go)
  • CEUS weights used in calculations
  • Some data screening needed to only use real fuel data and include desirable parameters
  • R-square = 0.77, F = 235
where to now
Where to Now?
  • Comparisons of CBECS and CEUS energy normalization methods indicate CA likely needs tougher adjustments than national-average-based methods provide
  • Several performance rating options will likely be available, including a sector-wide normalization tool, hopefully within a year
  • National sector-wide normalization tools also appear potentially important
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