Sense vs competitors

[kevin1]

@eshapurov,
You should take a look at my experiment with my AC units in the link below. I compared my Ecobee data for my 2 units against my Sense AC detections over the course of this past summer. It took several months for Sense to separate my two compressors into different detections and for those detections to become extremely consistent.

Sense vs. Ecobee: Round II

Well, cooling season is almost over and I decided to do one more monthly correlation analysis between my Ecobee’s hourly cooling runtimes and the power consumed by various Sense AC detections, AC, AC1 and AC2. FurnaceDown and FurnaceUp are hourly power used by the smarplugs monitoring each of my furnaces (mostly the furnace blower used by AC during the summer). Remember that I reset my Sense unit to ground zero in April, then put in new AC compressor units in mid-July. It’s interesting how the …

If you understand correlation you’ll be able to see the monthly evolution for both my downstairs and upstairs units. Down_Cool represents the hourly runtimes of my downstairs compressor taken from my Ecobee. AC, AC2, and AC3 are the hourly power for the 3 native AC detections from Sense. Furnace_ Up and Furnace_Down are hourly power for my upstairs and downstairs furnace blowers, as measured by smart plugs. Bottom line is patience - it took half the summer for Sense to sort things out.

$`2019-04`
            Down_Cool
AC                 NA
AC2                NA
AC3                NA
FurnaceUp          NA
FurnaceDown        NA

$`2019-05`
              Down_Cool
FurnaceDown  0.96457804
AC           0.40228237
FurnaceUp    0.38490212
AC2         -0.02461567
AC3                  NA

$`2019-06`
            Down_Cool
FurnaceDown 0.9923947
FurnaceUp   0.6044697
AC          0.3183210
AC2         0.2872561
AC3                NA

$`2019-07`
             Down_Cool
FurnaceDown 0.98625399
AC3         0.56490245
FurnaceUp   0.51139072
AC2         0.32950875
AC          0.06935545

$`2019-08`
            Down_Cool
FurnaceDown 0.9939775
AC3         0.7953435
FurnaceUp   0.6062248
AC2         0.1969622
AC          0.1884249

$`2019-09`
              Down_Cool
FurnaceDown  0.99038421
AC3          0.95855777
FurnaceUp    0.56123100
AC2          0.55809594
AC          -0.01585636

Up_Cool is the hourly Ecobee runtime data for my upstairs compressor.

$`2019-04`
               Up_Cool
AC           0.9791817
FurnaceUp    0.9769824
FurnaceDown -0.1541840
AC2                 NA
AC3                 NA

$`2019-05`
                Up_Cool
AC           0.99412114
FurnaceUp    0.97723727
FurnaceDown  0.14122794
AC2         -0.02226503
AC3                  NA

$`2019-06`
              Up_Cool
FurnaceUp   0.9892451
AC          0.6702095
FurnaceDown 0.5909811
AC2         0.1680601
AC3                NA

$`2019-07`
              Up_Cool
FurnaceUp   0.9850746
AC3         0.5987115
FurnaceDown 0.5123134
AC2         0.3918143
AC          0.1948437

$`2019-08`
              Up_Cool
FurnaceUp   0.9980361
FurnaceDown 0.6066122
AC3         0.5671459
AC2         0.4513146
AC          0.1011073

$`2019-09`
              Up_Cool
FurnaceUp   0.9968743
AC2         0.9321167
FurnaceDown 0.5697145
AC3         0.5522579
AC          0.1669208

AC3 eventually lines up with my downstairs compressor and AC2 eventually does the same with my upstairs unit.

ps: I don’t know anyone who understands machine learning, that would have doubts about Sense using machine learning to do identification and classification of significant power transitions. I do believe that there are devices that have non-causal signatures - signatures that don’t give enough information about what’s happening, but that’s not an AI problem. That’s a lack of data issue and is best solved by smartplugs.