Wow, glad to see people are really digging into this data, and fast.
I’ll throw 1 of mine in the mix, seems fairly basic compared to the processing above, but this has been incredibly helpful in tracking usage and $, as well as impact of any major changes.
I’ve essentially been tracking daily kWh usage manually, and comparing with total degree days (TDD), to trend usage with HVAC. I’ve found that my geothermal heat pump (GTHP) is by far the primary user in cold months, and there is a clear trend between HDD and kWh (and subsequently $/day). The trend, with an R^2 of ~0.9, allows me to accurately predict my daily / monthly bill based on weather forecast and historical/expected TDD.
Also, I made some changes in March, Success Story - Payback in <4 months, the effects of which can be seen in the shift between data sets (red to orange). The general trend follows a similar slope, but shifted down >10kWh / day. That alone brought approximately $45/month savings, with smaller incremental changes on top of that.
In cooling months, the GTHP is much more efficient, and therefore a lower percentage of the total daily use. So, the scatter for May-Sept is a shotgun pattern, without a clear discernible trend. Also, the GTHP usage is intermittent, as the cooling requirements are not only tied to average outside temps, but sunshine/cloud cover, etc. So in general, cooling requirements don’t trend as closely to TDD as heating.
This has been fairly quick to update semi-manually, but I suspect the data export will make all of this tracking nearly automatic.