Energy Efficiency
Part 1: Energy Star Offers New Ratings, Easier Benchmarking for Buildings
Part 2: Data Centers, Senior Care Now Eligible For 1 to 100 Energy Star Scores
Part 3: New Energy Star for Hospitals, Federal Involvement Among New Plans
New Energy Star for Hospitals, Federal Involvement Among New Plans
By Robert Sauchelli and Deborah E. Miller - September 2011 - Energy Efficiency
Two other important initiatives are underway this year. One involves an updated Energy Star scale for hospitals. To date, over 3,000 hospitals have received an Energy Star score for their buildings in Portfolio Manager. Since the current energy performance scale for acute care hospitals was developed using data from 1997, the American Society of Healthcare Engineering worked with EPA to collect more recent energy and operational data from its members. EPA is in the process of completing its analysis of the data to develop a revised energy performance score, which is expected to be released by the end of 2011.
In March 2011, EPA and Fannie Mae signed a memorandum of understanding to cooperate on improving the energy and water efficiency of the nation's multifamily housing stock through exploring the development of an energy performance scale for multifamily buildings. Depending on the outcome of the analysis, EPA anticipates rolling out an energy performance scale for the multifamily sector in late 2012 or early 2013.
As changes continue within the Energy Star program, outside factors are increasingly leading building owners to use the Energy Star rating. A good example: According to new regulations mandated by the Energy Independence and Security Act of 2007 (EISA), any space leased by the federal government as of December 2010, must be in buildings that have an Energy Star certification.
There are exceptions to that requirement: if no space is available in an Energy Star building that meets the needs of the agency, if the agency will remain in its currently occupied building, if the building is on the National Register of Historic Places, or if lease is for rentable square feet of 10,000 square feet or less. Nevertheless, the requirement is significant. The federal government is the largest tenant for leased commercial space, with over 370 million square feet under contract. What's more, if the building that an agency currently occupies is not Energy Star, the lessor may have to renovate the space with all energy efficiency improvements that are cost effective over the life of the lease.
There's a lot going on at Energy Star. Consider taking a few minutes to log on to the Energy Star commercial buildings website. Become a partner, use the many resources EPA has to offer, benchmark your buildings, and get Energy Star recognition. If you are looking for ways to improve your Energy Star score, evaluate whether using an experienced SPP will help.
Robert Sauchelli is Energy Star Buildings program manager, U.S. Environmental Protection Agency. Deborah E. Miller is vice president, ICF International.
EPA Committed to Keeping Energy Star on TrackMore than 21 billion square feet of commercial building floor space, representing close to 30 percent of the market, has been benchmarked in the Energy Star Portfolio Manager tool. The Energy Star score has been a valuable tool to motivate energy-use reductions and to identify top performing buildings. Recently, however, there have been problems with a little-known federal database that Energy Star uses. Despite those problems, the U.S. Environmental Protection Agency is confident that the Energy Star rating remains a valuable tool for building owners. That federal database — referred to as CBECS (Commercial Building Energy Consumption Survey) — comes from a national survey administered by the Department of Energy's (DOE's) Energy Information Agency. The survey draws from a statistically valid sample of the universe of commercial and institutional buildings and provides important building and energy data. Using CBECS, EPA made a startling discovery in the late 1990s: the gap between the country's best- and worst-performing buildings was greater than anyone had previously acknowledged — as large as tenfold. To help building owners understand how their buildings compare, EPA launched the Energy Star 1 to 100 scale in 1999 to provide feedback on where a building's energy use falls along this spectrum. That 1 to 100 scale is largely based on data from CBECS. Unfortunately, DOE recently announced that results from the most recent survey (2007) would not be published because it had not yielded valid statistical estimates. At the same time, DOE reported that, as a result of lower funding levels, it would temporarily suspend work on the survey scheduled for this year (2011). That means CBECS data continues to be drawn from the 2003 survey. Even with questions surrounding CBECS, Energy Star still offers relevant benchmarks. Here's why: 1. The 2003 CBECS survey still offers a solid benchmark. The rate at which new construction and retrofits replace building systems is slow. According to recent studies of actual energy use, new buildings can still perform more poorly than the CBECS 2003 average. 2. Each time EPA has analyzed the key drivers of energy use for offices, the major drivers were the same: workers, hours of operation, computers, size, and climate. This consistent result over 12 years suggests that the methodology underlying the Energy Star score, which is based on those drivers, remains sound. 3. Because the Energy Star score applies the same calculation to everyone, it remains a consistent means of placing all buildings on the same scale. 4. In addition to being an industrywide benchmark, the Energy Star score can also be used to track energy use of a building over time. Regardless of the age of the data on which the score is based, the actual number provides a uniform measure of how performance has changed. 5. While CBECS is used for many Energy Star scales, it is not the only data used by Energy Star. The scales for hospitals, senior care facilities, and data centers are all drawn from other surveys. EPA continues to work with industry to find other nationally representative data (or means of collecting data) to make Energy Star scales available for more building types. 6. As more buildings save energy, questions may be raised about whether it is becoming too easy to earn the Energy Star. If that becomes a concern, EPA could reset the minimum Energy Star score higher than 75. For all these reasons, EPA is confident that Energy Star continues to serve the market effectively. However, if the CBECS 2011 survey is cancelled, EPA will examine data and track trends of buildings using the Portfolio Manager tool and other surveys to assess market conditions and evaluate alternatives. It is possible to generate alternative data sets that are nationally representative, and EPA is willing to explore this option to ensure that Energy Star remains a valuable energy management tool. — Jean Lupinacci is chief, Energy Star commercial and industrial branch, U.S. Environmental Protection Agency. |
Comments
Marcel Harmon wrote re: Updated Energy Star Scale for Hospitals, Federal Involvement Among New Plans
on 9/15/2011 10:04:01 PM
I agree that Energy Star provides one of the best games in town for benchmarking, but would argue there is a great need for improvement. I think the baselines established by Energy Star and the energy modeling in general often over rely on national/regional averages and too generic of building types (i.e., K-12 as one category vs. elementary, middle school, and high school as just one example). Studies, such as the NBI's 2008 study, have found that while LEED facilities outperform non-LEED facilities on average nationally, on an individual basis LEED facility performance is all over the board, with a substantial portion not meeting design expectations. I think that part of the reason is due to individual facility's being compared to baselines that are unrealistic for a facility's actual context. A more realistic baseline would be based on local, peer group facilities, which is going to require an industry shift to more systematic evaluations of facilities post occupancy in order to gather this data to make the building process a more comprehensive and integrative process that includes the life of a facility. Occupancy shouldn't be seen as an endpoint, but as just another step in the process that continues through the life of a facility. Expanding the CBECS efforts to include more facilities nationwide and more sub-types of facilities is also needed to help with this.
The power point file, "Energy Star vs. Peer Group" (http://slidesha.re/qnWShm), graphically depicts my point here. Referring to the first slide/figure, a local average based on a local peer group could be significantly above or below the larger area/regional average. So facility B appears to be doing better than what the model, or Energy Star, or whatever is being used to establish a baseline predicts, but in actuality is performing worse than its local group of peers. Similarly, facility C appears to be doing worse than what the model predicts, but is actually performing better than it's group of local peers. Local facilities evaluated based on national/regional data (or data from the wrong peer group) may not tell you anything about how the facility is performing in reality and this could have significant repercussions with local municipalities and states adopting various energy efficient, green, LEED standards/regulations (particularly if these standards/regulations involve some type of post occupancy energy performance evaluation - what's the relevant baseline?).
The second two slides/figures depict a regression analysis of energy usage vs. facility square footage for an actual database of elementary schools from a single school district in Albuquerque, NM. The first slide is electric and the second gas. The blue line represents what the regression model predicts a given facility energy usage will be for a particular square footage of facility, based on this particular peer group of facilities. You can think of it as this peer group's average energy usage per square foot of facility, though it s a little more than that. Part of the regression analysis also provides an indication of how well the data fits the resulting model (blue line), or how well square footage was a predictor of energy usage, and in both cases the fit was very good (statistically speaking). In other words, for this dataset square footage was a good proxy variable for all of the factors that affect building energy usage.
The fourth slide/figure pulls out the individual data points for one particular school in this peer group (Edward Gonzales Elementary School), a high performance school that was being evaluated in terms of performance. I also used Energy Star to determine a baseline that I could compare to the peer group baseline, as well as the school s actual performance. The peer group model, based on square footage, predicts that an average school in this peer group of Edward Gonzales' size should be using 58.2 kBtu/sq. ft./yr (taking the electric and gas values in the previous two figures, converting to kBtu/sq. ft./yr, and adding them together). But the actual usage of Edward Gonzales was 76.4 kBtu/sq. ft./yr - the high performance school was underperforming its peer group (this is also reflected in the previous two slides/figures). The Energy Star baseline for this facility, though, is 88.8 kBtu/sq. ft./yr, significantly higher than the peer group baseline. Now I don t know for sure what was used as the baseline during design for this particular school, but I'm betting it was higher than the peer group baseline. Assuming it was in the range of what Energy Star provided, the results indicate the school was performing better than the modeled baseline (but not to the specific design goals of 30% to 50% below a standard, non-high performance school). Using a baseline that doesn't reflect local conditions means that your designing and measuring against an invalid baseline, setting the bar too high or too low. This poster presentation (http://slidesha.re/ngw3rp) provides an overview of this analysis as well.
