For one more fun experiment, I’m going to try to program my time-series model to categorize which car is charging based on the energy footprint over the “charging window”. I’ve tweeked my energy curves just a bit more, mainly to split the points in the curve below in what looks to be natural dividing lines.
Now I can compare against the “golden charging” data for 2020 where I tediously visually tagged every hour that looked like it had a car charging event. Note that I only identified a few hours as ‘None’, ones that had a big spike but proved not to be a charging event. I can merge my “golden” hourly classification data with my 15 min time-series classification data, but that ends up in a very unsatisfying confusion matrix. Why ? Because a “golden” hour labeled ‘Model 3’ merges with 4 different 15 min periods, of which only 1 might have been an actual ‘Model 3’ charging event for my time-series detector, and 3 'None’s.
Here’s the actual confusion matrix for all the 15 minute intervals common to the two datasets - vertically are my “golden” hourly classifications spread across all 4 15 min periods, horizontally my time-series detector which operates on 15 min periods.
If everything was perfect, we would just see values along the diagonal, where the 118, 282 and 71 are. But with the merge of two different time bases, it’s really OK to have a row where my time-series detector picked up 2 15 min periods of the ‘Model S’ but also 2 15 min periods of ‘None’, so the 2, 74 and 195 in the ‘None’ column don’t really concern me. The only two real issue might be the 31 differences of opinion on ‘Model 3’ vs. ‘Model S’, plus the one place where I saw ‘None’ but my time-series detector saw a ‘Model 3’.
If I look at a few of the waveforms, it becomes apparent that my time-series detector is probably better at classifying which car was charging, than I was when I did my “golden” labeling. Here are a couple of fun waveforms. My time-series detector results in the dots on the top of the waveform, my “golden” labeling in the line across the bottom. Notice that the detections line up fairly well though occasionally they are different colors (1 or more of those 31 differences)
Here’s a two week period where things line up really well.
Here’s one where I need to look at my time-series detector since it looks like it made a couple of mistakes with that first charging event.
A little more tuning of my edge detectors cleans up the time-series mistake on Jan 20, so now the detection and classification lines up perfectly with the “golden” data.
The confusion table also reflects the small improvement, moving the 31 ‘Model S’/‘Model 3’ conflations down to 21 15 min periods.
