Chair Elections and this Meeting

Jon Koliner

Hi CalTRACK Working Group,


Good meeting today! I’ll start with the pressing item first: 2020 Chair Elections. The candidate statements for our two nominees, Ethan Goldman and Natalie Kozlowski, are attached. Please read them over and submit your vote for our next Chair. As a process reminder:

  • Email Executive Director Bruce Mast at bruce@...
  • State your organization and which candidate you are voting for
  • Open for 1 week (to noon Pacific on 1/29)
  • Associate and Contributing Members may vote
  • 1 vote per organization, anonymous voting (your vote won’t be revealed to the group)


Here are the slides from today: Slides


As noted in the meeting, we are down a testing resource for 2020, and several organizations are in discussions about how to sustain the Working Group. This will likely lead to a delay before our next meeting, as we want the next meeting to be productive. The next Chair will be involved in those discussions and let everyone know when we’re starting up again.


In today’s meeting, we focused on the Working Group process and use cases for CalTRACK. Roughly speaking, the key takeaways were:

  • We should focus on improvements to methods that directly impact current programs, which means aggregated P4P portfolios in the near-term.
  • An explicit focus on those programs will help focus the group for future discussions and updates.
  • An explicit focus on the use of CalTRACK as a method that can be written into a contract (and understood to mean a specific set of methods, not a judgment-based analytical process) will help focus the group for future discussions and updates.
  • We could categorize issues into quick fixes that address bugs or deficiencies versus new additions to provide a “fast track” for needed improvements
  • We did not reach a determination on multi-stage modeling, machine learning, or the criteria by which we decide a model is interpretable (and therefore reasonable to include in the CalTRACK methods). However, we did note that interpretable models offer additional benefits beyond goodness-of-fit and prediction accuracy (e.g. quantifying seasonal load).


Remember to vote!




Jon Koliner

Apex Analytics, LLC

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