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By Indran Ratnathicam, FirstFuel Software
The U.S. produces an estimated 520 million tons of urban waste each year. And, if you have ever passed downwind of a landfill on a summer day, you likely are well-acquainted with the signature odor and huge, covered piles that give that number credence. Who is responsible? When it comes to the biggest trash-load culprits, pinpointing the offenders can be as basic as identifying the pre-landfill point of origin.
But when the trashed commodity in question is energy, the biggest “pileups” are unlikely to be overtly visual or odorous. Among the thousands of buildings in any large U.S city, it can be exceedingly difficult to pinpoint where the biggest inefficiencies are hiding. That leaves the commercial energy sector with two traditionally challenging questions. First, where should we look? And second, how can we do it intelligently and cost-effectively?
For utilities, many of whom are tasked with achieving annual energy efficiency targets, big buildings provide the natural answer to both questions. Based on energy consumption alone, the biggest buildings should naturally provide the largest savings opportunities. And the manageable number of large buildings in most commercial portfolios allows utility sales and account managers to cost-effectively engage these customers. But just how much of the savings come from these buildings?
Through analysis of thousands of commercial buildings since 2011, my colleagues at FirstFuel Software are starting to dig deep into the aggregate data. We uncovered an interesting finding about operational savings earlier this year, and we’re continuing to unlock insights to help utilities and government agencies better manage their efficiency planning and programs.
This time around, we looked at where efficiency opportunities exist across a typical commercial portfolio. A 60 million square foot sample of remotely audited commercial buildings revealed some interesting findings.
First, we found that 25 percent of buildings represent roughly 75 percent of total potential for energy savings. By itself, this insight may not be too surprising. We know that the variation in how buildings are operated, managed, and prioritized for efficiency improvements can vary dramatically. In addition, the large majority of commercial buildings in a given portfolio are small and micro-businesses with low consumption levels and savings potential. The classic 80/20 rule applied to commercial energy efficiency would naturally point to big buildings representing the high end of that 20 percent. But our data revealed a different answer.
Second, the biggest buildings don’t necessarily equal biggest savings. In fact, bigger savings may lie in mid-sized buildings. Data indicates that as much as 40 percent of portfolio savings opportunity actually is found in mid-size buildings— greater than the 35 percent found in the largest buildings.
This finding doesn’t mean that utilities – or government agencies, property managers, ESCOs etc. – should start ignoring big buildings. However, it does suggest that the traditionally low level of attention paid to mid-sized buildings - ranging from 50,000 to 150,000 square feet - should be reconsidered. Put more directly, mid-sized buildings represent a significant – and significantly overlooked – opportunity in commercial efficiency.
Even with this insight, utilities still face the other question: how can we identify the top efficiency opportunities intelligently and cost-effectively? Mid-sized buildings are more numerous than their bigger counterparts, and therefore require expending significantly more resources to uncover savings. The key is not to target mid-sized buildings as a whole (or large buildings as a whole either). The key is identifying which buildings are the top consumption offenders and which aren’t worth the effort. This is where advanced analytics can play a critical role. Effective analytics are the bridge between knowing there are needles in the haystack, and being able to hone in and pull the needles out in a single try.
The commercial energy sector will always be hesitant to pursue savings in smaller buildings. Yet, the data suggests that building size as a barometer is less important when remote analytics are utilized to uncover commercial savings. With the right data platform, utilities now have the tools they need to effectively navigate the mid-size building segment, swiftly and accurately pinpoint inefficient buildings, and open the door to a new, greenfield source of energy savings potential.
Indy holds an MBA from Harvard Business School and a BS from the Massachusetts Institute of Technology.
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