You might need to re-regress the fit lines. But with that change, the fit is much better as told by the residuals. In my case, point 33 and point 1 look like the worst. Point 33 is missing 1 hour, because I had to do the offset and only have the CMP data through 1/6/21 23:00. Picture the residuals as an exaggerated view of the points on either side of the fit line.
If I pull point 33 (1/6/21), both graphs look pretty good.
One thing that is highly noticeable, due to the way the automated labeling worked on these graphs, is the negative y-intercept between your Enphase and CMP net usage, similar to what we saw between Enphase and Sense on the pure solar side (Enphase was always seeing 450W more solar production than Sense, plus a 2.4% differential). If I look at look at the linear models for each pair, I see:
Enphase = (0.974 * CMP) - 0.741 Wh
Adjusted R-squared: 0.9999
Sense = (0.995 * CMP) + 195 Wh
Adjusted R-squared: 0.9997
Sense net usage looks to match your Utility net usage a little better than Enphase, but all three seem close. And I’m guessing from the solar analysis earlier, that Enphase always sees itself as producing a fixed amount more solar energy than is actually seen at the meter.
I think you have done a a good job showing that you can trust your utility meter net result. Now you are ready to get fancy and chart a few more months, all on one graph using CMP as the gold measurement.
The big gap between Sense and Enphase mostly stems from Enphase thinking it is producing more solar than Sense and CMP are able to see.