If you use Energy Star Portfolio Manager to assess and monitor building energy consumption you are likely seeing some score volatility since the August update to their data set (CBECS 2012). This and forthcoming posts should help you navigate the challenge of helping your customers understand the basis for their buildings’ changes (some dramatic) in scores.
Since the Energy Star Portfolio Manager energy score update in August, we’ve been asked to research quite a few changes in scores, some far greater than the averages shared by Energy Star when they released the update. As you may recall the following table was used to illustrate the average score impact by eligible building types.
In planning for the release, building owners were warned to consider:
Buildings may differ from the typical building used in this analysis, including if a building has
more than one use.
The change in a building’s ENERGY STAR score will vary from the published average depending on its energy use, fuel mix, business activity, property type, and other variables.
By definition, roughly half of buildings of any building type should have larger decreases than average, and half should have smaller decreases.
Let’s take a look back at why Energy Star needed to update its building data set (CBECS) and why some of the updated scores came in far lower than their previous scores. The graphic below illustrates that building scores inflated over time (blue line) and that the new CBECS data set (rust line) helped address the issue by doing a more equitable job of relatively evenly distributing the office building population across the 100-point spectrum.
Using office buildings as an example, the blue line illustrates that before the August ’18 update, through improvements in building energy efficiency, 50% of these office buildings had achieve Energy Star scores equal to or greater than 75. With the new, 2012 CBECs data, the EPA rebalanced the distribution of buildings so that now only 28% of buildings achieved a score of 75 or greater. And this redistribution is what is, in some cases, driving some scores down so significantly.
We knew that this massive redistribution of buildings was going to create some “winners” and “losers”. It wasn’t long before we began to see numerous examples. I see all of our incoming customer support emails and thought it might be valuable to share some actual experiences in the field.
I will cover examples in my next post and our team will continue to share examples of different building types in different regions.