EPA announces the 40% of Commercial space has been benchmarked

ENERGY STAR recently issued a press release stating that “Nearly 40 percent of the nation’s building space is benchmarked in Portfolio Manager, driving billions of dollars of energy savings while helping reduce greenhouse gases in the fight against climate change.”   Follow up questions to the EPA yielded additional information that Portfolio Manager has data representing about 300,000 unique commercial buildings containing 30 billion gsf of space.

Now according to the latest Commercial Building Energy Consumption Survey (CBECS) conducted in 2003 the nation has about 4.6 million buildings containing 70 billion gsf of space.  Ignoring any increase in the nation’s building stock since 2003, that would mean that the 40% of the nation’s gsf contained in Portfolio Manager belongs to only 5% of its buildings.

Below is a graph showing the distribution of U.S. Commercial building numbers (red) and gsf (blue) versus building size (in sf) as determined from 2003 CBECS data.  The graph shows that approximately half the total gsf is contained in just the largest 5% of buildings (> 50,000 sf).  You can also see that 40% of the gsf is contained in the largest 4% of the  buildings.  Portfolio Manager has data from 6% of the nation’s buildings.  If these are the largest U.S. buildings (> 44,000 sf) then this would represent about 52% of the gsf of the U.S. commercial building stock.  Instead, Portfolio Manager contains only 40% of the total U.S. gross square footage.  This means that Portfolio Manager must have data for about 80% of the nation’s buildings that are 45,000 sf or larger and only a tiny fraction of the buildings that are smaller.CBECS 2003 building distribution - ES2

Conclusion — Portfolio Manager and ENERGY STAR benchmarking is dominated by the nation’s largest buildings.

This raises concerns on several levels.  It turns out that the model that EPA uses for calculating Energy Star scores involves political decisions — what energy to allow for.  In moving from its 2003 to its 2007 Office model these decisions specifically enhanced the score for large office buildings — making it more attractive for large buildings to adopt Energy Star.

Secondly, the average ENERGY STAR score for all buildings that have submitted data to Portfolio Manager is said to be 62.  The supposed meaning of the ENERGY STAR score is a building’s percentile ranking as compared with similar buildings.  If this is the case then the mean ENERGY STAR score for all large buildings should be 50.  But if 80% of large buildings are in Portfolio Manager — how can their mean score be 62?  This might have seemed plausible if only a small fraction of large buildings were scored — but with such a large fraction is suggests a kind of “grade inflation.”  Apparently the ENERGY STAR score does not mean what we are told it means.

DOE Launches Building Energy Database — but why?

The US DOE has announced the creation of a Building Performance Database (BPD) with energy data for some 60,000 residential and commercial buildings across the U.S.  Presumably data for more buildings will be added as it becomes available.

But what is the purpose of said database?  Data included are not gathered randomly nor with any schema that will guarantee they are representative of any particular class of buildings.  Essentially any data submitted to the database are included so long as the data satisfy some specified criteria and cover a period of 12-consecutive months.  For some buildings data might correspond to 2003 and for others 2012 — with no distinction.

I have energy data for an Oberlin College building for 12 consecutive years — with site EUI ranging from 30 to 55 kBtu/sf/yr.  For which year shall I submit the data?  The idea that energy consumption for a building is determined by one 12-month period is silly.  And what is the usefulness of a query to this database that returns energy data for buildings for different time periods?

What interesting questions can be answered by query to such a database?  In this case having incomplete information is actually worse than having no information.  If you have no information you at least understand the limitation.  But having some building energy data without any information regarding its context is worse — leads you to believe you know something when you don’t.