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Energy Savings Potential Estimates Using CBECS and CEUS

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Energy Savings Potential Estimates Using CBECS and CEUS

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## Energy Savings Potential Estimates Using CBECS and CEUS

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### Energy Savings Potential Estimates Using CBECS and CEUS

Michael MacDonald

Oak Ridge National Laboratory

macdonaldjm@ornl.gov

ASHRAE SLC Annual Meeting, 6-25-08

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)

- 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

- 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 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

- 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

- 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 (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

- 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

- 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 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 . . .

- 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

- 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

- Heating and cooling degree-days
- Seating density for eating meals
- Hours of operation per week
- Personal computer density
- Worker density

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?

- 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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