Now that I know that the correlation between Sense’s output and my utility’s net meter is quite good, on aggregate within 2%, I’m going to look at detection/identification using the same techniques.
I only have two sets of devices in my house that offer accurate logs of on/off behavior:
- 2 Ecobees, upstairs and downstairs that offer a full set of HVAC usage on a 5 min interval.
- Hue lighting - which is already integrated with Sense, so I’m not going to even do a comparison here.
I’m also going to give my conclusions up front as well.
- Sense has done quite a good job of identifying AC condensers in my household.
- But the data also exposes what I believe to be second order challenges of unsupervised machine learning.
So here’s the tale of a love triangle between AC Up, AC Down and AC 3, three devices identified in my house by Sense. AC Up and AC Down first showed up in Sept 17, after I did a Sense data reset in Aug 17. AC 3 showed up on the scene later, in Jan 18, and changed all the AC relationships in my household
Here’s what things looked like in the idyllic days of 2017. Most of the hours of AC runtime measured from my Ecobees correlated extremely well with the corresponding energy usage identified by Sense. AC Up energy from Sense correlated beautifully with CoolUp runtimes from my Ecobee. Ditto for AC Down and CoolDown.
So far so good. The correlation lines are clear, with very few missed identifications, the data points where Ecobee shows a non-zero value but Sense shows zero (the dots lying on top of the x axis). We can even discern the power usage of both AC condensers by looking at the y intercept at x equals 1 hour (3,600 seconds). The upstairs unit runs at about 3.5kW, the downstairs unit around 5kW. Everything is working like it is supposed to, though the number of points is really quite small since this is Sept - Dec is typically not AC season, though the upstairs does tend to run warmer than downstairs due rising warm air, and sunshine heating the stucco.