A Peek Into the Time Series Approach
Before moving on, I should probably give a little bit more insight into my time-series approach that seems to work. The graph below gives some insight into how it operates. The red line is the 15min total energy usage from my utility extrapolated to a 1 hour value (I multiply the total usage for 15min x 4 so I can work with the 1 hour equivalent). I flag all deltas above a 6kWh equivalent change in 15min (vertical edges in blue). I then match up each positive edge with the nearest successive negative edge and look at the time distance between the two as well as mean energy used during the whole interval. If the distance is too far (more than 7 hours) or the mean is too low to be a car charging I reject the interval, leaving only “Good” charging intervals with the blue dots on top showing the mean.
The graph above shows two important things. First, the positive spike / delta on May 8th gets rejected because it’s nearest partner negative spike is 121 intervals way and the mean is too low. Second, this algorithm seems to work for back-to-back chargings of both EVs.
And here’s a slightly altered version of the chart I used earlier to help cull the “non-Good” charging intervals. All I did was reject any charge intervals that were greater than 7 hours and ones that didn’t have a mean energy usage that met the minimum charging rate for my Model 3, pre supposing no other energy usage in the house. The red line shows that minimum possible hourly charging energy for the Model 3 at 48A for a given number of charging periods assuming that the first and last 15 min periods have at least 2 1/2 minutes of charging. The blue line represent the the same for the Model S at 80A.
This chart will likely help me separate Model 3 and Model S chargings (as well as Boths) in the future. I also noted earlier that that the point in magenta is not good, even though it passed my tests, so perhaps a couple more border points slipped through. I’m going to examine the orange point as well.
I’m also going not need to go through the months of 2019 from a timeline perspective and see if I see anything weird. The goal is going to be to compare details in the Sense month against the details from my utility, as well as looking into places where my automated selections in blue look inconsistent.
Bottom line - for all you folks out there who claim that Sense could use some logic or a few heuristics to define or refine detections, you’ve got another thing coming… Not as easy as it looks.