About The Pragmatic Steward

Professor of Physics at Oberlin College. I was originally trained as a condensed matter experimentalist. In the last 15 years my research has focused on photovoltaic devices, PV arrays, wind energy, energy efficiency, and energy use in buildings.

Hot air emanating from the Windy City

This week Chicago mayor Rahm Emanuel hosted the North American Climate Summit attended by more than 50 mayors from major cities around the globe.  President Obama joined his old Chicago crony to address the summit.  Mayors joined together to sign the Chicago Climate Charter expressing their collective commitment to lower greenhouse gas emissions.

According to an article in HPAC Magazine Chicago Mayor Rahm Emanuel announced that Chicago had “reduced its carbon emissions by eleven percent from 2005 to 2015, bringing the city to forty percent of the way to meeting its Paris Climate
Agreement goals.”

What bullshit!  The same claim can be made by essentially every city in the United States (some more, some less).  This reduction has nothing to do with any unique accomplishments in Chicago — it is due to the simple fact that GHG emissions for the entire US from 2005 to 2015 went down by 11%.  All boats rise with the tide or, in this case, recede.

The main reason for this national GHG reduction is the fact that over the last decade cheap, fracked natural gas has replaced vast amounts of coal in the electric power sector.  This single change is responsible for the majority of the reduction in US greenhouse gas emission this last decade.  It isn’t energy efficiency, green buildings, renewable energy, or conservation — it is the economic impact of cheap natural gas and the increased cost of coal power due to EPA regulations.

Below is a graph lifted from an EPA report showing total US GHG emissions from 1990 through 2014.  The last bar for 2015 (black) was added by me using data pulled from another article.  The blue bars in this graph shows emissions associated with the electric power sector.

Rahm Emanuel’s claim is true but meaningless — just a lot of hot air emanating from the Windy City.

Federal Government Brags about Being Average

I recently ran across a post on the Energy Information Agency (EIA’s) web sit highlighting the fact that from 2003 to 2012 Federal buildings had achieved a greater decrease in energy use intensity than had been achieved by commercial buildings, on average. I find this spin to be offensive on various levels.

The relevant graph is shown below.

2016-09-16-government-building-eui

The first thing to note is that, even with this decrease in energy use Federal buildings still have higher EUI than do other commercial buildings (compare the red and blue 2012 bars).

Second, while I am pleased that the Federal government is learning how to operate its buildings almost as well as the rest of the commercial building sector, it is not a remarkable accomplishment.  It reminds me of a verbal exchange between then Governor Bill Clinton and businessman Ross Perot during a presidential debate.  Clinton was bragging that under his leadership the State of Arkansas had improved its rank among other states in education from almost last up to the middle of the pack.  Ross Perot pointed out that you don’t have to be innovative when you are ranked last — you will move up by just copying what others have done.  (I will confess, this is my memory of what happened, but it might be that I imagined this exchange — it is a good story, in any case.)

Third, why is the EIA engaging in such spin?  This agency is supposed to gather and disseminate energy facts.  Spin should be left to political parties.

 

The Illusions of EUI in Calculating Energy Savings

In the last month I have found the time to begin looking at the 2012 CBECS data released by the EIA last May.

Today I am writing about something I just learned concerning U.S. Worship Facilities.  Here I am looking at the subset of Worship Facilities that meet the criteria stated by the EPA for performing their multivariate regression for the Worship Facility ENERGY STAR model (about 80% of all U.S. Worship Facilities).

In comparing the 2012 and the 2003 CBECS data for Worship Facilities we see there was an estimated 2% increase in the number of these buildings.  As there is an 8-9% uncertainty in the estimated number of these facilities, this increase is  not statistically significant.  The EIA data show that the mean site energy use intensity (EUI) for these facilities actually went down by 15% from 48 to 41 kBtu/sf — and this reduction is statistically significant as it exceeds the 6-8% uncertainty in these figures.  No doubt some government agency will use this reduction to claim success in programs to promote energy efficiency.

But nature is not impressed because total energy used by these buildings actually went up.  The reason — the buildings are, on average, getting bigger!  From 2003 to 2012 the total gross square footage contained in this filtered subset of Worship Facilities increased from 3.2 to 3.8 billion sf, a whopping 23%.  Thus the total site energy used by Worship Facilities grew by 5%.  A similar conclusion can be made for source energy, even with the improved efficiency of the electric power sector over this last decade.

It should be noted that statistics show that the number of Americans who actually go to church declined by about 7% from 2007-2014.  So in a decade when religious worship is decreasing the amount of energy used by Worship Facilities has grown by about 5%.

Bottom line — don’t be fooled by decreases in building EUI.  It is total energy that matters.

New CBECS Data confirm EPA’s K-12 School ENERGY STAR score is nonsense

As I have written before — indeed, the subject of my recent book — my work shows that the EPA’s ENERGY STAR benchmarking scores for most building types are little more than placebos.  The signature feature of the ENERGY STAR benchmarking scores is the assumption that the EPA can adjust for external factors that impact building energy use.  This adjustment is based on linear regression performed on a relatively small dataset.  For most building types this regression dataset was extracted from the Energy Information Administration’s 2003 Commercial Building Energy Consumption Survey (CBECS).  The EPA has never demonstrated that these regressions accurately predict a component of the energy use of the larger building stock.  They simply perform their regression and assume it is accurate in predicting EUI for other similar buidings.

In the last three years I have challenged this assumption by testing whether the EPA regression accurately predicts energy use for buildings in a second, equivalent dataset taken from the earlier, 1999 CBECS.  In general I find these predictions to be invalid.    For one type of building — Supermarkets/Grocery Stores — I find the EPA’s predictions to be no better than those of randomly generated numbers!

In May of this year the EIA released public data for its 2012 Commercial Building Energy Consumption Survey.  These new data provide yet another opportunity to test the EPA’s predictions for nine different kinds of of buildings.  These new data will either validate the EPA’s regression models or confirm my earlier conclusion that they are invalid. Over the next year I will be extracting 2012 CBECS data to again test the nine ENERGY STAR benchmarking models based on CBECS data.

This week I performed the first of these tests for K-12 Schools.  539 records were extracted from the CBECS 2012 data for K-12 Schools representing 230,000 schools totalling 9.2 billion gsf.  After filtering these records based on EPA criteria, 431 records remain, representing a total of 137,000 schools with 8.0 billion gsf.

I performed the EPA’s weighted regression for K-12 Schools on this final dataset and obtained result totally inconsistent with those obtained by the EPA using CBEC 2003 data. Only 3 of the 11 variables identified by the EPA as “significant predictors” of building Source EUI for K-12 Schools demonstrated statistical significance with the 2012 data. Numerous other comparisons confirmed that the EPA’s regression demonstrated no validity with this new dataset.

The EPA will no doubt suggest that their model was valid for the 2003 building stock, but not for the 2012 stock — because the stock has changed so much in the intervening 9 years! While this seems plausible, this explanation does not hold water.  First, CBECS 2012 data do not suggest significant change in either the size or energy use of the K-12 School stock.  Moreover, this explanation cannot also explain why the EPA regression was not valid for the 1999 building stock — unless the EPA is to suggest that the stock changes so much in just 4 years to render the regression invalid.  And if that is the EPA position — then why would they even attempt to roll out new ENERGY STAR regression models for K-12 Schools based on 2012 CBECS data more than 4 years after these data were valid?  You can’t have it both ways.  Either the stock changes rather slowly and a 4 year delay is not important or this benchmarking methodology is doomed to be irrelevant from the start.

 

The more plausible explanation — supported by my study — is that the EPA’s regression is simply based on insufficient data and is not valid — even for the 2003 building stock.  I suggest a regression on a second, equivalent sample from the 2003 stock would yield results that differ from the EPA”s origina regression.  The EPA’s ENERGY STAR scores have not more validity than sugar pills.

 

“Building ENERGY STAR scores – good idea, bad science” book release

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.

rotated

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!

 

 

NYC Energy Benchmarking Report Over-estimates Energy Savings

The Mayor’s Office in New York City has recently released their annual report looking at the 2013 energy data for commercial buildings.  This is the fourth such report.  Each annual report appears to take longer and longer to prepare suggesting it is easier to gather energy data than to analyze and understand it.

The lead line in this report is that those preparing the report conclude that over a four-year period (2010-2013) green house gases associated with NYC building energy has decreased by 8% and energy use by buildings has decreased by 6%.  They cannot resist suggesting that NYC’s energy benchmarking program can take credit for this reduction.

My analysis of these data show the savings is only half this amount. The other half of the claimed savings is an artifact of the EPA’s having lowered its national, site-to-source energy conversion factor for electricity in Summer 2013.  The same mistake was made by the Washington DC Department of the Environment a year ago.

NYC does not live in a vacuum.  Over the last 10 years expanded use of natural gas and retirement of coal plants has cleaned up the entire U.S. electric grid — of which NYC is a part.  In fact, the purchase of fracked natural gas from Pennsylvania (fracking is outlawed in NY State) is the primary driver of reduced green house gas emission in NYC.  It has little to do with NYC building policies!

The NYC analysis apparently comes from adding up the annual greenhouse gas emission and weatherized source energy use of some 3,000 properties that submitted benchmarking data for all four years.  Using 2010 figures as a baseline the relative annual reductions for these selected properties are graphed below.

report figure 1

Here I want to focus on the source energy curve.  As compared with 2010, energy use went up slightly in 2011, then dropped by nearly 4% in 2012 and another 2.5% in 2013.  The drop in 2012 is easy to understand — hurricane Sandy brought the City to a grinding halt affecting tourism and many operations.  This reduction in energy use should be viewed with great skepticism.  But the continued reduction into 2013 seems like a sign of increased energy efficiency.  Or does it?

Until 2013 the EPA used a site-to-source energy conversion factor for electric energy of 3.34.  In summer 2013 the EPA adjusted this number by 6% to 3.14.  When it generated the 2013 report for NYC it used this reduced site-to-source energy conversion factor.  In other words, the 2013 reduction in NYC’s weather normalized source energy has little to do with building operation and everything to do with the EPA adjusting source energy down for the entire nation!  And this reduction does not reflect the single year improvement in the electric grid.  The EPA made no adjustment to this factor for many years prior to 2013, then in 2013 made a one-time-adjustment to reflect a 5-year average.

The NYC report is based on confidential data — no public benchmarking data were released for 2010.  Nevertheless, I can mimic the analysis by looking only at public NYC benchmarking data for 2011, 2012, and 2013.  In these data I find about 1200 buildings that reported energy data for each of these three years. About 1000 buildings remain after removing any that have questionable data for any of these three years (i.e., site EUI >1000 or <10 kBtu/sf).  The total weather normalized source energy for these buildings is graphed in blue below for each of the three years.  This graph mirrors the trend displayed in the NYC report.  The total site energy for these buildings is graphed in red.  The change in site energy matches the change in source energy for 2012 but not for 2013.  This confirms what I have explained above — that the EPA’s changing site-to-source energy conversion factor for 2013 is responsible for most of the change.  The graph below shows that 2013 site energy was actually higher than 2012 site energy.  It did not go down at all.

relative-energy-savings-scofield

The simple fact is that over the three year period shown below the site energy use of these 1000 buildings went down by only 3.5% — a figure which is highly uncertain given the sample size.  The 6% energy savings claimed by the Mayor’s Office is obtained through faulty analysis.

 

Energy harvesting — the siren’s allure

My wife, Deborah Mills-Scofield monitors dozens of media outlets and forwards articles to me that might be of interest.  One recently came my way about an effort in Portland, ME to harvest hydroelectric energy from its water pipes.  A company, LucidEnergy, has developed turbines that can be installed for this purpose.  The basic idea is to capture free energy in municipal water pipes that would otherwise be wasted.

While I applaud such innovation and creativity, I find the effort is misplaced.  I predict these turbines, like solar panels of the 1970’s and green roofs of this last decade — will soon be removed and abandoned.  This kind of energy harvesting is a fool’s errand.

About a decade ago I learned about another energy harvesting project in Israel — to install piezo-electric tranducers in highways to capture energy from passing trucks.  As heavy vehicles passed over these tranducers the truck weight would cause the transducers to compress and produce electricity.  The promoters of this energy argued that normal road compression represented lost energy — their technology would capture energy that would otherwise be lost.  The installed transducers did, in fact, produce electricity.  But I am confident that careful analysis would show that this energy comes from slight increase in fuel consumption of the vehicles that pass over the transducers.  Highway rolling resistance is mostly due to compression of the tires, not the road surface!

I am not aware of any evidence that water passing through municipal pipes arrives at end destinations with excessive kinetic energy.  Therefore any energy harvested along the way is likely to have to be re-injected by pumps.

And the maintenance issues must be significant.  I envision a few years of testing at the end of which it will be concluded that the cost of maintaining these units far exceeds the value of the energy they generate.  And what about the maintenance of pipes which get plugged due to low flow velocity?

Nature has handed us sunlight, wind, and hydo energy.  Harvesting these abundant resources is proving to be a challenge.  Harvesting efforts should focus on these well-understood and low-maintenance options.

Humans clearly waste a terrific amount of energy.  And there are many different ways that this wasted energy might be harvested.  The problem is cost-effectiveness.