FYI - I’m noodling over a time-series thresholding approach to detecting charging ramp using my 15min utility data, to label the 2019 data set, rather than going through the long manual labeling process (with me as the Mechanical Turk). Even if that labeling is not 100% correct, I could play the two prediction approaches against each other to isolate on the boundary data that matters, in a slightly different form of Adversarial Machine Learning (two predictors compete to get the right answer, instead of one generating and the other predicting).
But it’s not as easy as it looks. This week in May 2020 is a bit emblematic of the challenge. A bunch of Model S charges (18kW), some very short, one long, along with one medium duration Model 3 charge (11kW).
If I had Sense’s granularity of data, I could probably pick a single usage threshold that would catch all the Model S charge. But a fixed threshold wouldn’t discriminate between Model 3 charing and noise in the house at other times.
Level Detection - Here’s the same thing with a 15min sample view, instead of the Sense 1/2 second resolution. The color of the dots actually highlights the charging state for that corresponding hour. What you will notice is that some the of short (timewise) Model S charging periods actually register less energy usage than some of the 15min non-charging periods the same day. So with a 15 minute sampling, one can’t rely on energy usage exclusively, even for the beefy Model S charging.
Edge Detection - And if I chart deltas (the difference between current usage and the previous 15min period), most of the charging ramps stand out, except for the Model 3 ramp, that comes just after the Model S finishes on May 2nd. It really looks like one long charging session.
So whatever I do with the time series approach, it is going to need to blend edge detection with level detection to give a complete picture.