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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

Michael MacDonald

Oak Ridge National Laboratory

[email protected]

ASHRAE SLC Annual Meeting, 6-25-08

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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

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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

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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

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CBECS and CEUS, some important differences

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Basic EUI Statistics kBtu/sq-ft per yr

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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

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Energy Performance Methods estimates

  • 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

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ASHRAE Handbook, Fundamentals estimates

  • 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?)]

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Economic-Engineering Models estimates

  • 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

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Page 34 estimates

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Simultaneous Multivariate Normalization Compares Performance estimates

  • 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

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Building-Type Models estimates

  • 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

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Sector-Wide Models estimates

  • 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

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Or Other Types of Models . . . estimates

  • 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

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CBECS National Model Form estimates

  • 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

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Basic CBECS Model Parameters estimates

  • Heating and cooling degree-days

  • Seating density for eating meals

  • Hours of operation per week

  • Personal computer density

  • Worker density

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California CEUS Model Form estimates

  • 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

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Where to Now? estimates

  • 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