Energy Savings Potential Estimates Using CBECS and CEUS

1. Energy Savings Potential Estimates Using CBECS and CEUS Michael MacDonald Oak Ridge National Laboratory macdonaldjm@ornl.gov ASHRAE SLC Annual Meeting, 6-25-08

2. 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

3. 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

4. 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

5. CBECS and CEUS, some important differences

6. Basic EUI Statistics kBtu/sq-ft per yr

8. 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

9. 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

10. 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?)]

11. 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

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13. 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

14. 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

15. 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

16. 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

17. Example of possible grading

18. 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

19. Basic CBECS Model Parameters • Heating and cooling degree-days • Seating density for eating meals • Hours of operation per week • Personal computer density • Worker density

20. Building / Space Types Adjusted

22. How is California Isolated?

23. Savings Potential based on CBECS Performance Model Scores

24. 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

25. California Potential Savings

26. 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