Electricity quality or noise score

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OK - here’s what I think happens in Sense-land, but only speculation:

First the math:

  1. From the specs - Each Sense produces up to 4 million 2 bytes samples / sec - I assume the fastest rate is for Sense with Solar. I’m also guessing that the sampling is asymmetrical with current getting many more samples than voltage because the power company essentially forces the voltage on the lines to be 120V AC, and has to respond to current needs to maintain that AC voltage level.
  2. 1 month of operation produces 8MB/sec x 2.6Msec/month of raw data = 21TB / month
  3. From data I have seen on the forum, Sense uploads about 3.6GB / month. That’s about a 6000x reduction. Still a lot to sort through.

My suspicion is that the Sense monitor does a lot of number crunching to reduce the uploaded data. I’m thinking it feeds the Sense mother ship with:

  1. A steady stream of 1/60th second averaged power data for each phase
  2. A stream of highly processed, but detailed data on transition events that fall into select parametric windows - windows that match up with fundamental physics identification.
  3. A separate stream of 1/2 second readings from all of the smartplugs that the Sense monitor is conversing with.
  4. All the needed stuff to manage and control the monitor’s operation
  5. Maybe local calculation and uploading of Other and Always On as well ??

I surmise this based on a few of the screens that I have seen in open videos of Sense’s internal tools, that show detailed power waveforms for each leg, plus cluster diagrams that show multiple dimension clustering of transition events.

Here’s where it gets tricky:

  • Most of the detection processing is done on that transistions/events database. Sense uses clustering and perhaps dozens of features gleaned from those transitions to classify discernible clusters (transitions that are parametrically very close across some set of those features).
  • Once Sense has found a discrete cluster with some certainty, it becomes a detection. Sense then compares that cluster against it’s crowd-sourced database of clusters for closeness and produces the category choices via logistic regression.
  • Once a cluster has been identified/categorized, Sense takes that cluster, including all new entries from newer fitting transitions, out of consideration as a new device. To be clear, the removal of the device does NOT come from subtracting off waveforms from smartplugs or existing detections, but from removing in the cluster space.
  • Sense might do some removal of transitions it “sees” on a smartplug from the transitions data pool, to reduce detections of devices already on smartplugs, but I’m guessing there are some timescale challenges to doing so reliably all the time. So if Sense does this, putting a smartplug on a noisy device might help remove datapoints that are unlikely to deliver meaningful clusters.
  • But the larger noise issue remains - a noisy device transition might often step on a smaller real physics transition. If they both get blended, due to overlap, there’s no way to reconstruct the real-physics based one using information from the smartplug.
  • Over time, Sense might tweak with which transition features it extracts using the monitor or some mod the weighting it uses to detect clusters based on additional training and model development. Plus things may change in your house that cause new categorizations to occur as well as new detections.
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