The first thing many people ask when they first install their Sense is “How accurate is Sense vs. my utility monitoring?” We can now begin to answer that, at least in the case where one’s utility allows them to download usage data. I’m going to walk through my accuracy analysis process for all the existing data I have for 2018.
In my case, I have solar, so I have two utility data sources:
- An hourly net meter energy reading from my electric utility, PG&E, via their “Green Button”. Unlike some solar installs, I don’t have a second meter looking at my total house usage - I’ll only be able to compare Sense net usage vs. the net usage from my PG&E meter.
- An hourly solar energy reading from my SolarEdge inverter. The data is downloaded via my SolarCity / Tesla portal. Unlike PG&E and Sense, the SolarCity download capability only allows hour resolution downloads a day at a time. I had to write a little script to download all 224 days in 2018 I was interested in, then merge them all together.
Once the data was in, I did a little processing in R,
- Converting the vertical narrow Sense data format into a wide time vs. device format and extracting only the Total Usage and Solar Production columns
- Calculating my Sense net usage (Sense Total Usage + Solar Production, since Solar Production is negative)
- Calculating the difference (Diff) between PG&E net usage - Sense net usage
- Calculating the percentage difference (PerDiff) between PG&E net usage - Sense net usage
- Aggregating the data into daily data so I could also compare that.
An example of wide Sense data below - Note all the NAs (not available) for hours where there was no energy value available in the Sense export for those devices.
Here are the initial results for Sense net usage vs. PG&E net usage in a scatter plot. A high percentage of the data correlates quite well. The slope of the line is almost exactly 1 at both the hourly and daily level. But there are a significant number of outlier data points, especially at the hourly level, where the Sense power values are well below the PG&E values. And based on color, the furthest outliers occurs in March and June. There also seems to be a peculiar upward curving tail in the negative end of the hourly curve. I’ll investigate both of those in depth a little later.
One more look at the accuracy - If I look at a histogram of my net PG&E usage minus Sense net-usage difference, two things become apparent.
- There’s a long, but very thin “error tail” on the positive side, where the PG&E net is much greater than the Sense net.
- But huge majority of the differences are within a very narrow band around zero. Based on my histograms, I’m only going to look in detail at the 217 hourly mismatches (out of 5421 total) greater than 200W to start with.
Hourly difference is less than 500W
Hourly difference is less than 200W