Image courtesy of Flickr user Stuck in Customs

The early results from New York's new energy disclosure law show the promise -- and the current limitations -- of energy benchmarking.

New York’s ambitious plan to cut greenhouse gas emissions by 30% focuses, unsurprisingly, on the built environment. In 2009, New York passed a benchmarking ordinance requiring all privately-owned building greater than 50,000 square feet to report energy and water usage. The initial results provide a fascinating glimpse of the opportunities and flaws of current benchmarking initiatives.

New York City benchmarking data of energy use intensity by building type
Source: NYC Mayor’s Office

Variation in energy intensity across five sectors

The benchmarking survey reveals massive variance across buildings in energy intensity, which in this study is defined as kBTU per square foot, a measure that allows disparate energy sources, such electricity, natural gas, and steam, to be rolled up into a single figure. The most efficient office space, for example, uses 78% less energy per square foot than the least efficient.

Benchmarking shines a spotlight on the worst performers and helps prioritize efficiency efforts. As the study notes, bringing the least efficient buildings up to the performance level of the median performers would shave 18% off the city’s energy consumption. A lot of improvements can be made cheaply through improved operations and maintenance, such as calibrating sensors and controls.

New York City data of building energy use intensity by the year built
Source: New York University

Energy Star score for office buildings based on year built.

At least, that’s the theory. In reality, most buildings are reasonably well-managed, and the disparity in energy intensity figures could very well reflect meaningful differences in building use. Energy intensity scores tell an interesting story about the built environment, but it’s not always clear what conclusions to draw. For example, one counterintuitive finding from the survey is that Energy Star scores are best in the oldest buildings, and worst for the newest. Haven’t building codes gotten tighter in the last hundred years? Hasn’t building technology improved? Several explanations suggest themselves:

  • Perhaps older buildings have better thermal envelopes.
  • Perhaps older buildings are simply less comfortable, colder in the winter and hotter in the summer.
  • Perhaps older buildings are more cramped than newer ones. Energy Star normalizes for occupancy, so more densely packed buildings will get better scores.
  • Older buildings were built before “data center” was even a phrase. Perhaps newer buildings carry a disproportionately high computing load.

What’s needed is more precise benchmarks, which in turn require the fifteen-minute interval data now being delivered by smart meters and other advanced metering infrastructure. Detailed load curve analysis might reveal, for example, that baseload is much higher in some buildings than others, suggesting that computer servers are to blame. Or interval data analysis might show high sensitivity to occupancy patterns, implicating lighting and plug load. Extreme weather sensitivity, on the other hand, would suggest that energy managers should focus on HVAC.

Fifteen-minute interval data paints an incredibly rich picture of building operations, and dozens of operational benchmarks are possible: chiller sequencing, start and stop time, night-time and weekend setback, and so on. Benchmarking studies understandably have to make do with the data available, which to date has mostly meant monthly bills. Smart meters change the equation, and one of the most exciting — if least hyped — uses of the technology is much finer-grained benchmarking analysis that can address the question not just of where to seek efficiency gains, but how.

About Adam Stein

Adam runs the product team at Gridium. Formerly he co-founded TerraPass, and before that worked at Tellme and Trilogy. He has an MBA from Wharton and a BS from Stanford, neither of which impress his young daughter.

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