Smart meter analytics has huge potential for improving the way utilities and program managers evaluate the performance of energy efficiency programs.

Traditional evaluation methods involve combining "deemed" savings from theoretical models - which may or may not be realistic - with occasional verification from a small sample of participating locations. Even these samples often do not actually measure that which is being targeted: changes in property energy consumption. 

In short, the performance metrics of most efficiency programs are essentially determined before a single measure has been implemented. We do not know how much energy has been saved by most energy efficiency programs simply because it has not been measured.

Luckily, this situation is entirely avoidable and the solution is clear: use hourly AMI meter data to actually determine how the energy consumption changes in properties that participate in programs.

Let's take a look at one example. Note - the below analysis is not intended to be a full-scale EM&V project, but is simply designed to demonstrate the power of AMI meter data. See the bottom of the post for a more thorough disclaimer!

Chart 1: Average summer load shape for weatherized homes vs comparable peer group.

Chart 2: Average summer load shape for insulated homes before and after weatherization program completed.

But wait - what about weather effects? Couldn't they explain the difference between one summer and the next?

Absolutely - so let's try to get a rough measure of what those weather effects might be, so we can factor them into our analysis. Chart 3 below, shows the single family average load shape for the same two summers as used in the "before and after" chart shown above. Any differences in these shapes will be due, to a large degree, to changes in weather (see caveats at bottom of post). 

Chart 3: Average summer load shape for all single family homes before and after weatherization program completed.

To summarize, the results of method 1 (peer group) suggested a 17% energy saving while the results of method 2 (before-after) suggested a 9% energy saving. Not bad! 

The analysis presented here took the author less than a couple of hours to conceive, conduct and summarize in this post. High quality program evaluation need not be an expensive or difficult undertaking. Utilities already have the data they need and the tools are readily available. Let's get started!

Important caveats: we did not attempt to be scientific in our selection of peer group homes, we simply controlled for some sources of obvious error (e.g. homes with PV solar systems). A full EM&V project would need to select peer groups for each individual measured home, rather than comparing groups to one another. We also recommend that all EM&V projects use weather normalization to counter the confounds of different temperature profiles for different time ranges. This analysis used a simple way to infer weather effects, but would not be robust enough "in the real world".

Note - all analysis contained in this post was conducted using the SageSight analytics platform.