We’ve (well, I’ve mainly, with other people rolling their eyes more than likely) had some discussions on solar production calculations and errors therein and I just wanted to consolidate it to one thread that might hopefully help solar customers recognize the issue and determine whether or not it’s affecting them. According to @HilarioAtSense in this post, Sense has recognized the problem and are working on a fix, pending identification of the users affected by the issue.
This thread is meant to be a summary of my findings and the means by which other solar users might identify whether they are affected or not, starting with a very quick and simple method and progressing through a couple more labor intensive methods. I figured that providing the data and how the calculations were made would allow other solar users to better grasp what I’ve been babbling about for the past two months.
The least labor intensive way to identify whether you’re affected by this is to take a look at your usage graphs from a distance, by manipulating the graphs to cover 12+ hours. What you may notice, if you have a home that’s typically quiet at night when you’re sleeping, and during the day when you’re at work, is that the baseline power consumption is different, and notably during the day takes a bit of a dip that’s correlated to the magnitude of your production values. A composite of the past 48hrs from my Sense unit is shown below. Note the very consistent baseline during the night - that’s my fridge mostly humming along, in contrast to the “smile,” or “daytime dip” produced during the day, where that baseline drops in tandem with the rise in solar production. This is because the solar CTs are improperly calculating the power coming in from the solar panels; the power being calculated from the main CTs is spot on, so while it’s accurately measuring the power going back out through the main CTs to the grid, Sense is secondarily miscalculating the power the house is using (because the information it’s getting from the solar CTs is incorrect).
That’s the easy way to identify the issue, but it requires a pretty calm house at night and during the day to identify. Another way that’s a little more labor intensive and requires a secondary measurement device - possibly your inverter if it’s able to provide the information, is to compare the solar production graphs between the Sense device and your secondary device. This was actually my first indication there was an issue. I went back through my Sense readings and identified the production values on a 15min schedule…mainly because that was as frequently as my inverter was reporting values. I then graphed my inverter 15min readings against my Sense 15 min readings, and over the course of a few days what became obvious was that Sense was reporting production values about 3.5-4% below what my inverter was reporting:
The most labor intensive way I was able to ferret out the problem was by taking twice daily readings of my meters, just before sunrise (on the hour!) and just after sunset (on the hour!), to make sure I was separating out the numbers achieved when the sun was up and we were making most of our power vs. when the sun was down and all of our power was coming from our utility. This requires a bit more effort, and a meter (or meters) which allow you to determine (1) your solar production, (2) how much of that solar was sent back to your utility, and (3) how much power you are using from the grid. In my case, I have two meters - one that measures solar power and a number of other solar-related values, and a second that measures how much power we’re sending back to the grid and how much we’re using from the grid, among other grid-related values. You will have to check with your utility to identify how you may do this at home using your meter(s).
By taking pre-sunrise and post-sunset readings of my meters (on the hour, because that’s how Sense divides things!), I was able to build a spreadsheet (linked below) to develop calculations that allowed me to chase down where the error was occurring. You can use this spreadsheet as well, if you have access to the data I mentioned above. You will have to enter your own meter readings, and laboriously calculate the Sense values from your daily trends graphs by pressing on each bar and getting it to read out a value for you for that particular hour.
To fill out the spreadsheet, you’ll enter your solar production meter info, your utility power reading, and your solar out reading. Your solar production, energy in, energy out, and total energy usage will be calculated from these meter values automatically. Next, you’ll enter the data you calculated from your Sense trends. Enter your Sense solar production values and your Sense energy usage values for the daylight and nighttime periods. The rest will be calculated for you - the discrepancy in production (in my case ranging from ~3-4+%) and the raw values. What I found was that, again, Sense was under reporting how much solar I was producing, and that was directly impacting how much Sense was reporting as used during the day - the raw values were equivalent, meaning that if Sense was low by 1.5kwh on solar, my daily usage was also low by 1.5kwh. (See the values for “usage discrepancy raw error” and “Solar Sense raw error” highlighted in pink)
A link to a Google spreadsheet is here, because I’m not able to upload a spreadsheet. You should be able to copy and paste it to your own xcel file, but please let me know if you’re interested in doing so and cannot - it only has permission to view, not to edit, though that should still allow you to copy-paste. The information you’ll need to enter on your end, apart from the date, is highlighted in red.
If you’ve gone through the first two steps and found obvious differences between what Sense is reporting and what your inverter or meters are telling you, the third step should help you confirm what you’re seeing. You’ll be looking to see that nighttime values are matching up well between your meters and Sense, while daytime values are off. You should see that the raw value for solar production error is equivalent to the raw value for daily usage error, highlighted in pink on the spreadsheet.
Thanks for following along, and let me know if you have any questions or comments.