Comparing All Detections !
Now comes the fun part. I have merged 2019 and 2020 and pulled together detections from all my different sources:
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Sense - 3 different detections from different models at different points in time (orange = EV, purple = Model S, magenta = short-lived Tesla detections). For these, the colored dots show the energy level detected for those hours.
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My simple GLM-model machine learning detector - dots in green. Even though it is simple, it is highly custom for my house. It hasn’t been generalized for any other homes.
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My time-series based, hand-tweaked detector. For these two years, it’s almost 100% accurate on detecting charging (I still see one visible error), but doesn’t posit charging level (yet). Plus it’s only that accurate if we ignore extremely short, low energy charging events, some that I can’t even visually resolve. Note that I can’t do confusion matrices for each of the Sense detections since I wasn’t very careful about tracking when the specific device models were added and deleted (EV/Electric Vehicle is my only current surviving device model). Once again, this model is highly custom and not generalizable to any other homes or EVs.
Here are some of the most interesting 2 week periods.
EV (orange) doing its job today. It really only triggers with the Model S, and predicts about half the energy actually used. The GLM model does a good job with these two weeks, finding all charging events and avoiding other spikes.
A few mistakes by both the EV detection and the GLM detector.
Here’s a case of both Model S (purple) and EV (orange) predicting against one another. Energy predictions are typically way low.
Another similar 2 week period.
Model S (purple) does a good job discriminating between Model S and Model 3 charging cycles. But it also comes up with a fair number of low energy false positives…
One of the few shots of the Tesla detector (magenta) in action. It aligns with Model 3 charging, and seems fairly specific to just Model 3 events, plus it nails the energy fairly closely.
