Re: Implementing Fractional Savings Uncertainty (FSU)
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1) It looks to me like you are interpreting the equation correctly. The intuition for why the combined FSU is lower than either of the individual FSUs is that more data generally leads to lower uncertainty. It may be helpful to remember that the FSU is a fractional/normalized value, quantifying the uncertainty relative to the level of savings. The non-fractional or non-normalized total savings uncertainty adds like variances do, that is, by taking the root of the sum of the squares. So the total uncertainty will be increasing, but by less than the total value of the savings, and thus the ratio of these decreases.
Hi Nhi,Thanks for reaching out. I'll do my best to answer your questions, although I will ask for some clarification on the second one.
2) I'm not sure I completely understand the intent - do you want to know the expected FSU for a portfolio of 100 sites? or something else?
2a) I can point you this document from the CPUC that describes "Normalized Metered Energy Consumption Working Group Recommendations for Population-Level Approaches" which lists the 25% threshold. I will also ask around at Recurve if there is a publication or public dataset that can be shared to back up the statement read on the website, but I can confirm from personal experience that it is a reasonable expectation for programs with either deep savings or a very large number of projects (or both). Because the FSU is divided by the savings value, you can expect higher FSU values if you're expecting lower percent savings (this would be the case for 1% savings - you will likely find in your bootstrapping analysis that you will need a lot more projects to hit this threshold than you would with deeper savings).
On Wed, Mar 11, 2020 at 1:33 PM <nhi.ngo@...> wrote: