Thanks as well for sharing yours. Your Always On appears much better behaved and predictable than mine during 2018, especially since you can correlate the what looks to be a downward slope in the beginning of the year with you actively doing Always On reductions. I’m guessing the bimodal pattern indicates hidden changes in the Always On algorithm. And that your low end outliers during the first 6 months (and second 6 months), are really data dropouts.
As for your questions about mine:
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I do another export in a couple days - what I see from the bubbles is that my new normal is around 210W, but sometimes export reveals stuff the bubble does not.
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As for finding dropouts, I cooked up a web app data scraper that looks at the Power Meter for every day, and counts the number of dropout events and negative usage events visible on the screen. Finally have a way to automatically find virtually all the dropouts. More here, including code: