After more than three years in the making I have finally published my book, Building ENERGY STAR scores — good idea, bad science.  This book is a critical analysis of the science that underpins the EPA’s building ENERGY STAR benchmarking score.  The book can be purchased through Amazon.com.  It is also available as a free download at this web site.

I first began looking closely at the science behind ENERGY STAR scores in late 2012. The issue had arisen in connection with my investigation of energy performance of LEED-certified office buildings in New York City using 2011 energy benchmarking data published by the Mayor’s office.  My study, published in Energy & Buildings, concluded that large (over 50,000 sf) LEED-certified office buildings in NYC used the same amount of energy as did conventional office buildings — no more, no less.  But the LEED-certified office buildings, on average, had ENERGY STAR scores about 10 points higher than did the conventional buildings.  This puzzled me.

So I dug into the technical methodology employed by the EPA for calculating these ENERGY STAR scores.  I began by looking at the score for Office buildings.  Soon thereafter I investigated Senior Care Facilities.  Over the next three years I would dig into the details of ENERGY STAR models for 13 different kinds of buildings. Some preliminary findings were published in the 2014 ACEEE Summer Study on Energy Efficiency in Buildings.  A year later I would present a second paper on this topic at the 2015 International Energy Program Evaluation Conference (IEPEC)  Both of these papers were very limited in scope and simply did not allow the space necessary to include the detailed analysis.  So I decided to write a book that contained a separate chapter devoted to each of the 13-types of buildings.  In time the book grew to 18 chapters and an appendix.

This book is not for the general audience — it is highly technical.  In the future I plan to write various essays for a more general audience that do not contain the technical details. Those interested can turn to this book for the details.

As mentioned above the printed copy of the book is available through Amazon.com. Anyone interested in an electronic copy should send me a request via email with their contact information. Alternately an electronic copy may be downloaded from this web site.

Incidently, the book is priced as low as possible — I do not receive 1 cent of royalty.  The cost is driven by the choice of large paper and color printing — it was just going to be too much work to re-do all the graphs so that they were discernable in black and white!

To say that someone “lied” is a strong claim.  It asserts that not only is the statement false but the person making it knows that the statement is false.

The EPA revised and updated its ENERGY STAR Technical Methodology document for Medical Office Buildings in November 2014.  That document makes the following claims:

1. it describes filters used to extract 82 records from the 1999 CBECS
2. it claims that the model data contain no buildings less than 5,000 sf in size
3. with regard to the elimination of buildings < 5000 sf the EPA writes, “Analytical filter – values determined to be statistical outlyers.”
4. the cumulative distribution for this model from which ENERGY STAR scores are derived is said to be fit with a 2-parameter gamma distribution.

All of the above statements/descriptions are false.  The filters described by the EPA do not produce an 82 record dataset, and the dataset produced do not then have the properties (min, max, and mean) described in Figure 2 of the EPA’s document.  And a regression using the EPA’s variables on the dataset obtained using their stated filters do not produce the results listed in Figure 3 of the EPA’s document.  In short, this EPA document is a work of fiction.

I have published these facts previously in my August 2014 ACEEE paper entitled “ENERGY STAR Building Benchmarking Scores: Good Idea, Bad Science.”  Six months ago I sent copies of this paper to EPA staff responsible for the agency’s ENERGY STAR building program.

I have given the EPA the opportunity to supply facts supporting their claims by filing three Freedom of Information Act (FOIA) requests, the first (EPA-HQ-2013-00927) for the list of 1999 CBECS ID’s that correspond to their 82-building dataset, and the second (EPA-HQ-2013-009668) for the alpha and beta parameters for the gamma distribution that fits their data, and the third (EPA-HQ-2013-010011) for documents justifying their exclusion of buildings <5000 sf from many models, including Medical Offices.  The EPA has closed the first two cases indicating they could not find any documents with the requested information.  17 months after filing the third request it remains open and the EPA has provided no documents pertaining to the Medical Office model.  The EPA is publishing claims for which they have no supporting documents and that I have demonstrated are false.  The details of my analysis are posted on the web and were referenced in my ACEEE paper.

In November 2014 the EPA corrected errors in other Technical Methodology documents yet it saw no need to correct or retract the Medical Office document.  Why is it so hard for the EPA to say they messed up?

It is common for scientists to correct mistakes by publishing “errata” or even withdrawing a previously published paper.  No doubt EPA staff once believed this document they have published was correct.  But how is it possible the EPA remained unaware of the errors while it continued to publish and even revise this document for nearly a decade?  How can the EPA continue to publish such false information six months after it has been informed of the errors?

Is the EPA lying about its Medical Office building model?  I cannot say.  But it is clear that the EPA either has total disregard for the truth or it is incompetent.

If these follks worked for NBC they would have to join Brian Willams on unpaid leave for six months.  Apparently the federal government has a lower standard of competence and/or integrity.

On January 28, 2015 the District of Columbia published the second year of energy benchmarking data collected from private buildings.  This year’s public disclosure applies to all commercial buildings 100,000 sf and larger while last year’s public disclosure was for all buildings 150,000 sf or bigger.  Data published are drawn from the EPA’s ENERGY STAR Portfolio Manager and include building details such as gsf and principal building activity along with annual consumption for major fuels (electric, natural gas, steam), water, and calculated green house gas emission (associated with fuels).  Also published are annual site EUI (energy use intensity) and weather-normalized source EUI metrics, commonly used to asses building energy use.

The District Department of the Environment has analyzed these two years of data and concluded the following:

• DC commercial buildings continue to be exceptionally efficient. The median reported ENERGY STAR® score for private commercial buildings in the District was 74 out of 100—well above the national median score of 50.
• Buildings increased in efficiency from 2012 to 2013. Also,  overall site energy use went up by 1.5% among buildings that reported 2012 and 2013 data. However, when accounting for weather impacts and fuel differences, the weather-normalized source energy use for the same set of buildings decreased by 3% in 2013.

These claims are simply unjustified.

In particular consider the second point — that 2013 source energy used by DC buildings is 3% lower than it was in 2012 — demonstrating improved energy efficiency.  This claim is based on weather-normalized source energy numbers produced by the EPA’s Portfolio Manager.  The problem is that the EPA lowered its site-to-source energy conversion factor for electricity from 3.34 to 3.14 in July 2013 — a 6% reduction.  Because of this simple change, any building that has exactly the same energy purchases for 2013 that it did in 2012 will, according to Portfolio Manager, be using 4-6% less source energy in 2013 (depending on the amount of non-electric energy use).  In other words — the District finds its buildings used 3% less source energy in 2013 than in 2012 when, in fact, by doing nothing, all US buildings saved 5-6% in source energy over this same time frame.

It is said that “a rising tide lifts all boats.”  In this case the Washington DC boat did not rise quite as much as other boats.

More seriously, such small differences (1% – 3%) in average site or source energy are not resolvable within the statistical uncertainty of these numbers.  The standard deviations of the 2012 and 2013 mean site and source EUI for DC buildings are too large to rule out the possibility that such small changes are simply accidental, rather than reflective of any trend.  Scientists would know that.  Politicians would not — nor would they care if it makes or a good sound bite.

Let me now address the other claim.  It may well be true that the median ENERGY STAR score for district buildings is 74.  I cannot confirm this – but I have no reason to doubt its veracity. But there are no data to support the assumption that the median ENERGY STAR score for all commecial buildings is 50.  All evidence suggests that the national median score is substantially higher — in the 60-70 range, depending on the building type.  My recent analysis shows that the science that underpins these ENERGY STAR scores is wanting.  ENERGY STAR scores have little or no quantiative value and certainly DO NOT indicate a building’s energy efficiency ranking with respect to its national peer group — despite the EPA’s claims to the contrary.

The claim that the median score for US buildings is 50 is similar to making the claim that the median college course grade is a “C.”  Imagine your daughter comes home from College and says, “my GPA is 2.8 (C+) which is significantly higher than the (presumed) median grade of 2.0 (C).  You should be very proud of my performance.”  The problem is the actual median college grade is much closer to 3.3 (B+).  Its called grade inflation.  Its gone on for so many years that we all know the median grade is not a “C.”  Until recently ENERGY STAR scores were mostly secret — so the score inflation was not so apparent. But the publication of ENERGY STAR scores for large numbers of buildings as a result of laws such as those passed in Washington DC has removed the cloak — and the inflation is no longer hidden.

ENERGY STAR scores are no more than a “score” in a rating game whose ad hoc rules are set by the EPA in consultation with constituency groups.   It seems to have motivational value, and there is nothing wrong with building owners voluntarily agreeing to play this game.  But like fantasy football, it is not to be confused with the real game.

A few  weeks ago NYC released Energy Benchmarking data for something like 15,000 buildings for 2013.  9500 of these buildings are classified as “Multifamily Housing” — the dominant property type for commercial buildings in NYC. While data from Multifamily Housing buildings were released by NYC last year, none included an ENERGY STAR building rating as the EPA had not yet developed a model for this type of building.

But a few months ago the EPA rolled-out its ENERGY STAR building score for Multifamily Housing.  So this latest benchmarking disclosure from NYC includes ENERGY STAR scores for 876 buildings of this type.  (Apparently the vast majority of NYC’s multifamily buildings did not qualify to receive an ENERGY STAR score — probably because the appropriate parameters were not entered into Portfolio Manager.)  Scores span the full range, some being as low as 1 and others as high as 100.  But are these scores meaningful?

Earlier this year I published a paper summarizing my analysis of the science behind 10 of the EPA’s ENERGY STAR models for conventional building types including: Offices, K-12 Schools, Hotels, Supermarkets, Medical Offices, Residence Halls, Worship Facilities, Senior Care Facilities, Retail Stores, and Warehouses.  What I found was that these scores were nothing more than placebos — numbers issued in a voluntary game invented by the EPA to encourage building managers to pursue energy efficient practices.  The problem with all 10 of these models is that the data on which they are based are simply inadequate for characterizing the parameters that determine building energy consumption.  If this were not enough the EPA compounded the problem by making additional mathematical errors in most of its models.  The entire system is built on a “house of cards.”  The EPA ignores this reality and uses these data to generate a score anyway.  But the scores carry no scientific significance.  ENERGY STAR certification plaques are as useful as “pet rocks.”

Most of the above 10 models I analyzed were based on public data obtained from the EIA’s Commercial Building Energy Consumption Survey (CBECS).  Because these data were publicly available these models could be replicated.  One of the models (Senior Care Facilities) was based on voluntary data gathered by a private trade organization — data that were not publicly available. I was able to obtain these data through a Freedom of Information Act (FOIA) request and, once obtained, confirmed that this model was also not based on good science.

Like the Senior Care Facility model, the EPA’s Multifamily Housing ENERGY STAR model is constructed on private data not open to public scrutiny.  These data were gathered by Fannie Mae.  It is my understanding that a public version of these data will become available in January 2015.  Perhaps then I will be able to replicate the EPA’s model and check its veracity.  Based on information the EPA has released regarding the Multifamily ENERGY STAR model I fully expect to find it has no more scientific content than any of the other building models I have investigated.

One of the problems encountered when building an ENERGY STAR score on data that are “volunteered” is that they are necessarily skewed.  Put more simply, there is no reason to believe that the data submitted voluntarily are representative of the larger building stock.  ENERGY STAR scores are supposed to reflect a building’s energy efficiency percentile ranking as compared with similar buildings, nationally.  When properly defined, one expects these scores to be uniformly distributed in the national building stock.  In other words, if you were to calculate ENERGY STAR scores for thousands of Multifamily Housing Buildings across  the nation, you expect 10% of them to be in the top 10% (i.e., scores 91-100), 10% in the lowest 10% (i.e., scores 1-10), and so on.  If this is not the case then clearly the scores do not mean what we are told they mean.

Meanwhile, it is interesting to look at the distribution of ENERGY STAR scores that were issued for the 900-or-so Multifamily Housing facilities in NYC’s 2013 benchmarking data.  A histogram of these scores is shown below.  The dashed line shows the expected result — a uniform distribution of ENERGY STAR scores.  Instead we see that NYC has far more low and high scores than expected, and relatively fewer scores in the mid-range.  24% of NYC buildings have ENERGY STAR scores ranging from 91-100, more than twice the expected number.  And 31% of its buildings have scores 1-10, more than 3X the expected number.  Meanwhile only 12% have scores ranging from 41 to 90.  We expect 50% of the buildings to have scores in this range.

Of course it is possible that New York City just doesn’t have many “average” Multifamily Housing buildings.  After all, this is a city of extremes — maybe it has lots of bad buildings and lots of great buildings but relatively few just so-so buildings.  Maybe all the “so-so” buildings are found in the “fly-over states.”

I ascribe to the scientific principal known as Occam’s Razor.  This principal basically says that when faced with several competing explanations for the same phenomenon, choose the simplest explanation rather than more complicated ones.  The simplest explanation for the above histogram is that these ENERGY STAR scores do not, in fact, represent national percentile rankings at all.  The EPA did not have a nationally representative sample of Multifamily Housing buildings on which to build its model, and its attempt to compensate for this failed.  Until the EPA provides evidence to the contrary — this is the simplest explanation.