Tim Guiterman from Sealed led off yesterday's discussion with a follow-up conversation on revising CalTRACK's requirements to permit its usage with delivered fuels such as propane and heating oil. Because these fuels are delivered and manually refilled, data for them are not as consistent nor as abundant as metered energy is. Tim and the Sealed team proposed making an exception to the 70 day data limit for delivered fuels.
The group discussed what the minimum number of data points should be and various factors that could alter this. There was also discussion of relaxing the 365 day baseline period to allow for more data when fitting this period. Most delivered fuel customers can provide data in the form of bills that cover years of time. Adam pointed out that while there might not be a perfect answer to the delivered fuel problem, using the CalTRACK approach is still much better than deemed approaches which are much less accurate.
The conversation concluded with Tim agreeing to analyze Sealed data so that the working group can make a data-driven decision on if rule changes should be made for delivered fuel customers and if so what should the minimum requirements be, with proof that backs up these revisions.
Adam and Travis then summarized CalTRACK 2.0's deficiencies and how the nearly final model of CalTRACK 2.1 rectifies them. The main problem the team was attempting to solve was seasonal bias in the CalTRACK 2.0 model found primarily in gas meters. They showed that CalTRACK 2.1 has significantly reduced seasonal bias. At the same time it has been necessary to revisit how fast the model can be fit in. Travis has implemented several clever means of speeding up the model from worst-case scenarios of 1 minute to fit down to 10 seconds. He also went into detail about how CalTRACK 2.1 differs from CalTRACK 2.0 in order to solve the issues laid out prior. Additionally, Travis briefly mentions that they have implemented a CalTRACK 2.0 mode, a legacy mode, which can replicate CalTRACK 2.0 results with all of the speed improvements developed recently.
Overall, they show that CalTRACK 2.1 reduces seasonal bias by 74% and is between 2 and 100 times faster than CalTRACK 2.0, depending on how it's used (legacy mode or not).
Adam and Travis anticipate that by the next meeting, the final CalTRACK 2.1 model will have completed its R&D phase. Next steps involve code cleanup, polishing, and documentation so that CalTRACK 2.1 can be integrated into the OpenEEmeter.
Next Meeting Scheduled: Tuesday, June 6th, 1pm PT.
Watch the full presentation below.