Will Robots Take My Job?
With the IEPEC 2017 conference coming soon in August, evaluation as a concept has been on my mind a lot. Like a stereotypical millennial I’ve been nibbling avocado toast and feeling a little existential … wondering if there’s still a need for energy efficiency evaluation… for me… with all the recent talk about “game-changing,” “paradigm” shifting advanced M&V, M&V 2.0, non-intrusive load meters (NILMs), and machine learning computers that will optimize program implementation…and take over my job.
Is there still a path for my budding and what I thought was a promising and long career in energy efficiency evaluation?
When I asked my colleagues, the experts, and my mentors at DNV GL, like Amit Kanungo and Tammy Kuiken, the answer was a pretty resounding YES.
In fact, they’ve doubled down on it, quite literally. Recognizing the need to address how the industry is changing, Amit and team extended the workshop that they’ve been teaching at IEPEC for the past five years from half a day to a full day this year. The expanded workshop content will focus on how we can adjust to these changes in our evaluation world, a reflection of the transition in the larger energy industry. They see this point in time as an opportunity to evolve, not to act as a road block, and embrace the new tools with promises of better and faster evaluations with a grain of caution, as any good evaluator would.
Amit’s workshop, on Monday will focus on the existing evaluation best practices as they relate to end-use monitoring and address how these new industry disruptors can be harnessed and added to our evaluator bag of tricks as an enhancer… while understanding and working to address their limitations, as with any other tools.
After the workshop, it’ll be worthwhile to check out Tammy’s poster on Tuesday evening, on a case study about using NILM to monitor whole-house end-use and what worked and didn’t. If I had to tweet about it, I would say something like: the hardware for detecting the end-use loads making good on promises; the machine learning analytics could use a little more time maturing.
While I’m a little uncertain about these disruptors, it’s also exciting to know there are these unknowns for me and my colleagues to solve and work out in the foreseeable future. So, you’ll still be seeing me and other DNV GL-ers at IEPEC in a month talking, tweeting, and linking-in about accurate and actionable evaluation.