Conflated Devices

[kevin1]

@quedak,

Would appreciate any more specifics you have on heuristics for detecting a 13A and above usage ramps and dropoffs. I’m trying to build an accurate edge detector that “finds” my EV charging cycles. I don’t have access to the same high res usage data as Sense, but I figure my every 15 min read from smart-meter merged with Sense data should be enough, given the 48A and 80A draws of each EV. I have one machine learning model “working” on my 2020 data, but now I need a way to label my 2019 data, since I didn’t collect charger info in 2019. Thoughts appreciated.

Simple Machine Learning EV Charge Detectors

Since I had already collected and tagged my combined Sense/utility data with EV charging events, I decided to try a simple experiment to see if I could build a “detector” to tell if one of my EVs (Model S and Model 3) was charging during a given hour. A few key points of context: Sense has been detecting the Model S fairly reliably throughout 2020, so far, but I don’t really have a fix on how reliably. Sense was detecting the Model 3 for a while in 2019, but kind of gave up in 2020. I haven’…