The Heisenberg Uncertainty Theory of Energy Efficiency, and Aggregated NMEC in CA
- February 7, 2019
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Last week, the California Public Utilities Commission (CPUC) released new guidance that effectively greenlights the application of Normalized Metered Energy Consumption (NMEC) to aggregate (population-level) energy efficiency programs.
Learn more about this ruling from this OpenEE Blog.
Why is this super complicated weedy regulatory stuff so important?
Think about it this way: Aggregated Normalized Metered Energy Consumption (NMEC) is the Heisenberg's uncertainty principle applied to energy efficiency.
The uncertainty principle states that you cannot know, with absolute certainty, both the position and momentum of an electron – the more accurately you measure one of these properties the less accurate your knowledge of the other.
The traditional approach to energy efficiency is based on measuring every attribute, controlling for every variable, and modeling the physics of each building in an attempt to attribute savings to the individual measure, and from that, model the overall impact to the greater system. While very costly and complicated, it's also very poor at predicting system-level outcomes. Lots of precision, very little accuracy.
Aggregated NMEC is the opposite approach. Aggregated NMEC measures everything at the meter and generates a statistical and probabilistic model of the overall system that is an excellent predictor of outcomes at the portfolio or aggregate level, but trades off the ability to attribute savings to measures or know with certainty the impact on individual buildings.
Moving to aggregated NMEC enables performance-based regulation and accurate forecasting, and creates the confidence to bring energy efficiency into DER procurements and manage risk to attract project finance -- at drastically lower costs to all parties.
That's a big deal.