There have been a number of great discussions about EM&V here at the ACEEE Summer Study this year. In addition to some good papers in the formal sessions each morning, there have also been at least two well-attended informal sessions on the topic. I held an informal session on Monday and attended another hosted by Ellen Franconi of Rocky Mountain Institute on Tuesday.
While the Monday session had perhaps an evaluator’s bent and the Tuesday session spent a fair bit of time discussing the merits of terminology “2.0”, there is demonstrated agreement within the industry that both the measurement of actual savings and the evaluation of programs should be taken to a new level. Numerous parties on Zondits have written a number of articles about it in recent months, and I will not revisit those. But if you follow the embedded links you will find our ideas about ubiquitous data harvesting and EDGE M&V.
My takeaway from all of it is that while E, M, & V seem to be three letters forever joined in the EE space, to understand where it is headed, perhaps we should dissect it into E and M and V…
E is an important piece of the energy efficiency program world. We must evaluate programs to ensure that our efforts deliver successful energy efficiency. Vast sums of ratepayer funds are in play and we must quantify actual savings and understand the how and why of the savings actually achieved. In addition to a need for faster turnaround times and shorter cycle times, we are increasingly entering an era of energy efficiency as capacity where we must assure reductions at specific hours of need, defend load reductions to T&D and supply planners, all while we continue to ensure that energy efficiency is the most cost-effective resource.
M generates a lot of discussion, and while many of the “M&V 2.0” discussions focus on whole-building AMI data to provide measurement data, access to detailed in-building data is becoming easier and more affordable‒and is just so much more valuable. Evaluators need data, and in many cases whole-building AMI data will need to be augmented with additional data sets. Fortunately, the ability to persistently measure energy savings can now be accomplished in a wide variety of ways. Data streams delivered to the cloud by independent IoT devices inside buildings will be cost-effective, ubiquitous, and valuable for harvesting of rich data sets, far beyond kWh and kW readings, to enable identification of micro-trends within buildings and macro-trends across portfolios. Just who will measure what and for whom is the subject of much discussion and remains to be seen. With new pressures on baseline definitions and growing interest for tracking progress by a variety of stakeholders, the data that constitutes measurement may come from even more places than it already does. Implementers, building owners, and systems providers all have reason to be part of this equation. Regardless of what data is added to the mix and who actually does it, how we get the data that constitutes M is in a state of flux.
V, for some, is reduced to the physical confirmation and verification that stuff exists, while others expand it to include more of the analytics as well. M and V are frequently closely joined, almost regarded as one in efficiency parlance. Access to detailed data sets with occupancy, system state, and energy usage information, coupled with advanced analytical tool sets and frameworks, offers an opportunity for enhanced verification and tracking of efficiency with deeper insights into causes and effects, successes and challenges. Such in-depth and detailed verification and automated analysis will boost confidence in energy efficiency as capacity, as a resource, as a bankable activity, and allow effective investigation of localized trends. In many cases analytics at this level will be highly automated, will involve machine learning, and will yield better, faster, cheaper, and more meaningful results.
As we all work to deliver more value to administrators, regulators, facility owners, investors, implementers, system planners (and the list goes on), a key to all of this will be data. While I do not want to downplay the analytics and machine learning aspects, which will be huge in their own right, getting the data from the spaces and equipment in buildings to “the edge” and up to “the cloud” becomes a central task. This distinct world of high granularity data, accessible in the cloud to be worked upon with the latest analytical methods, aims to leverage the latest in-building sensor and behind-the-meter monitoring technologies to produce the highest value results.