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Getting Reporting Right: Tips for Solving for Gaps in Your Energy and Sustainability Data

April 9, 2013 By Jørgen Vos and Pablo Päster

Jørgen Vos and Pablo Päster

Gaps in reported energy and sustainability data are a common challenge.These gaps are the result of leased locations where utility bills are paid indirectly, or can be the result of insufficient or nonexistent data collection systems and processes. Solving for gaps is critical to getting internal and external sustainability reporting right. Such gaps represent a risk to the quality and completeness of an organization’s GHG emission inventory and company investor disclosure (through the Carbon Disclosure Project), and they can also jeopardize the entire decision-making process around an organization’s investments in energy efficiency.

Existing Estimation Tools

The most comprehensive source for building energy intensity metrics is the Energy Information Administration’s Commercial Building Energy Consumption Survey (EIA CBECS, http://www.eia.gov/consumption/commercial/). Historically released every four years, the CBECS is emerging from a series of technical and budgetary setbacks. The 2007 survey results were recalled after a “cheaper but experimental survey frame and sampling method by EIA’s prime contractor” resulted in data that did not “meet EIA standards for quality, credible energy information.” (email, 5/6/2011, “2007 Commercial Buildings Energy Consumption Survey (CBECS) will not be Released”) Then in 2011, CBECS faced a “budget delay” and is now working on a 2012 survey to be released in 2014. This means that, until the middle of next year, the most recent US commercial building-wide survey data is from 2003.

Beyond this, the data that users of CBECS rely on is based on relatively small sample sizes within each facility type, and intensity values that are based on a single variable at a time (electricity per square foot for a hospital of any size anywhere in the US, or electricity per square foot for any building of any size in a specific climate zone). Clearly a better and more granular methodology is required to obtain relevant intensity factors for estimating building energy use.

Estimations and Reporting Protocols

Most reporting mechanisms make accommodations for estimated data. For example, according to the GHG Protocol “Emission estimates are acceptable as long as there is transparency with regard to the estimation approach, and the data used for the analysis are adequate to support the objectives of the inventory.” (GHG Protocol, p 31, http://www.ghgprotocol.org/files/ghgp/public/ghg-protocol-revised.pdf).

The CDP (formerly Carbon Disclosure Project) 2013 reporting schema does not specifically mention estimates, but requires the submitter to estimate the level of uncertainty present in their submitted data, and to describe the sources of such uncertainty. The UK’s CRC (Carbon Reduction Commitment) describes acceptable estimation methodologies and adds 10% to all estimated data to account for potential errors.

Energy Star’s Portfolio Manager accepts the use of estimated data with one upcoming exception. With the summer 2013 upgrade of Portfolio Manager, Data Centers must have metered data in order to receive an Energy Star score. If a data center occupies more than 10% of an office building, Portfolio Manager will require this data center to be sub-metered.

Estimation Best Practices

Energy and resource data estimates are generated using various best practice methodologies. Before creating an estimate to fill a data gap, you have to ask yourself a number of questions, such as:

  • How do I know that I have a data gap? Do I expect my usage records to have continuous data entry with back to back bill end and start dates?
  • What do I use as the source of estimation benchmarks? My own data, or external data such as CBECS?
  • What data quality requirements do I have? Are the CBECS benchmarks “good enough”, or is my own historical data more appropriate? Is my sample size of comparable data large enough to be representative?

Using historic data from the same site as you are trying to create an estimate for may be appropriate if you have historic data available that is representative of the gap you are trying to estimate and where:

  • Multiple years of data so you can see the amount of variation present
  • Small annual and/or seasonal variation in the data
  • The use of the building has not changed significantly and no significant energy efficiency projects have taken place

Using data from a single comparable organizational unit may be acceptable if the comparable org unit is of the same facility type (e.g. office), approximately the same size, or uses the same equipment (HVAC, lighting, computers etc.) and utilities.

Using data from multiple comparable org units is preferable, especially where a large enough sample size of comparable sites is available (typically >5), those sites have complete data sets for the estimation period, and site information is available for climate zone, area (square feet), facility type, etc.

In all cases, the energy use is estimated by area (square feet) and by time period to normalize the energy use into an intensity factor that is then multiplied by the area of the site to be estimated and the amount of time for which there is a data gap.

Note that you need to make sure that you count the number of days in a time period correctly. If you use Excel to calculate the number of days difference in a utility billing period, take special note to validate whether you need to include the entire starting day and ending day for the period in your calculations, rather than taking the difference which would reduce the count of number of days by one.

A special case of estimation is one where a facility operator is able to correlate their utilities usage to business activities, weather, or a combination of these and other factors. Companies that know this correlation exists often hire consultants to calculate so-called regression formulae for them. There will be a different formula for each utility type used in a facility, and there will typically be a different formula per utility for each facility.

An example of such a regression formula in the case of a hotel operator could be:

Monthly Electricity Use (kWh) = [A Baseline Number] + [A Multiplier] x Number of Room Nights Sold + [Another Multiplier] x Cooling Degree Days + [Another Multiplier] x Number of Dinners Served.

Regression formulae tend to be more useful for forecasting purposes and for evaluating the effectiveness of energy efficiency projects than for filling data gap estimates. The reason for this is that in order to calculate regression formulae, you need to have reliable data available so you would not find many data gaps in sites for which regression formulae exist.

A Necessary Skill

Estimating energy use for gaps in historical data and for leased facilities based on a broad range of unique requirements is an integral part of creating a comprehensive greenhouse gas inventory. Larger and more diverse enterprise building portfolios may need to apply multiple estimation methodologies. Managers need to document their methods and the inputs used in order to pass 3rd party audits.

There are a number of estimation tools on the market that enable in-house energy and sustainability professionals to estimate energy use for gaps in historical data and for leased facilities based on a broad range of unique and methodologies as outlined here. An understanding of how and where gaps must be filled for effective reporting is the first step, and then tools can allow for further flexibility and automation in the chosen estimation methodology.

Pablo Päster is senior director of account management, and Jørgen Vos is director of product management, for Hara Software.

 



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