Inspired by this thread, I took a stab at comparing the actual production from my solar panels over the four months I have complete data against what the installer estimated the production would be. All the usual caveats apply, especially the fact that four months' of data isn't really enough to draw any conclusions from. But I found the results interesting nevertheless, so I figured I would share.
First Stab
So the first thing I did was simply compare the actual production in September through December against the monthly production estimates from the installer.
Solar-Analysis-1.gif
This shows that in September and October we produced a lot more power than estimated, and in November and December we produced less. It makes sense that we had less production in November and December, since we had a big run of cloudy weather those months, and one of the arrays was covered in snow for a significant amount of time during those months.
Second Stab: Snow Cover
On my house we have 16 solar panels arranged in two arrays. One of the arrays is over the garage and easily reachable with a brush on a long pole, the other array is three stories off the ground. Since the beginning of winter I've been keeping the garage array brushed off after every snowfall, and also keeping a log of which days the house array has been snow-covered.
Because I kept one of the arrays clean and the two arrays are monitored separately in our eGauge, it's possible to estimate how much production we lost because of snow cover (I used the measured production from the clean array to estimate what the snow-covered array would have produced if I had been able to brush it off).
Solar-Analysis-2.gif
This is showing that we lost a fair percentage of November/December production to the snow cover (about 25% and 20%, respectively). This is not surprising, and probably pretty typical for a Minnesota winter. Up here, after it snows it tends to get very cold for a while, and from what I've observed so far this winter it needs to get up to about 25F and sunny before the snow and ice will loosen and slide off the array. It's possible to go weeks after a snowstorm before we get weather that warm.
Last year before signing the installation contract, I asked the installer how he accounted for snow on the panels. His response was that he just applied a fudge factor and reduced the annual production by a few percent. That's not a very satisfying approach, but maybe the best you can do given how unpredictable snow cover is.
Third Stab: Cloudy Weather
In this post, sensij provided a link to a nearby weather station where I could download solar radiation data since mid-2007. This gave me a way to try to figure out how much of the deviation between our actual production and the estimate might be due to the cloudy weather we had in November and December.
So I downloaded all the insolation data from that weather station and calculated monthly averages for 2007-2014, and used that to estimate how much more or less solar radiation we received in each month of 2014 relative to the average of the prior seven years.
Solar-Insolation-History.gif
This estimate is fairly rough because (a) I only had seven years' of data for calculating the historical average (20-30 years is more typical for calculating climactic averages), (b) the weather station is 15 miles away so may have gotten somewhat different weather, and (c) I didn't even attempt to account for any time of day effects or anything more granular than the monthly average insolation.
But it did give me a way to quantify how much cloudier/sunnier each month was relative to the longer term average. So I used that to adjust the installer's original estimate, by just multiplying the production estimate by the percentage of average radiation received. So for example, in October when we received 112% of the average radiation, I multiplied the installer's estimate of 352 KWh by 112% to get an adjusted estimate of 395 KWh.
Solar-Analysis-3.gif
What Does It Mean?
Four months' of data isn't enough to draw any conclusions, so this is really more a fun exercise in data analysis than any kind of realistic assessment of how accurate my installer's estimate was. Nevertheless, here's a summary:
It seems pretty clear to me that my solar panels are producing a lot more power than the installer's original estimate. In September and October, when there was no snow cover, we outperformed by a huge margin even though September was cloudier than average. In November and December we produced less than the estimate, but still a lot more than I would have expected given the cloudiness and snow. Even with all the assumptions and approximations I made in this analysis, generating 30% more power than expected is a big gap.
First Stab
So the first thing I did was simply compare the actual production in September through December against the monthly production estimates from the installer.
Solar-Analysis-1.gif
This shows that in September and October we produced a lot more power than estimated, and in November and December we produced less. It makes sense that we had less production in November and December, since we had a big run of cloudy weather those months, and one of the arrays was covered in snow for a significant amount of time during those months.
Second Stab: Snow Cover
On my house we have 16 solar panels arranged in two arrays. One of the arrays is over the garage and easily reachable with a brush on a long pole, the other array is three stories off the ground. Since the beginning of winter I've been keeping the garage array brushed off after every snowfall, and also keeping a log of which days the house array has been snow-covered.
Because I kept one of the arrays clean and the two arrays are monitored separately in our eGauge, it's possible to estimate how much production we lost because of snow cover (I used the measured production from the clean array to estimate what the snow-covered array would have produced if I had been able to brush it off).
Solar-Analysis-2.gif
This is showing that we lost a fair percentage of November/December production to the snow cover (about 25% and 20%, respectively). This is not surprising, and probably pretty typical for a Minnesota winter. Up here, after it snows it tends to get very cold for a while, and from what I've observed so far this winter it needs to get up to about 25F and sunny before the snow and ice will loosen and slide off the array. It's possible to go weeks after a snowstorm before we get weather that warm.
Last year before signing the installation contract, I asked the installer how he accounted for snow on the panels. His response was that he just applied a fudge factor and reduced the annual production by a few percent. That's not a very satisfying approach, but maybe the best you can do given how unpredictable snow cover is.
Third Stab: Cloudy Weather
In this post, sensij provided a link to a nearby weather station where I could download solar radiation data since mid-2007. This gave me a way to try to figure out how much of the deviation between our actual production and the estimate might be due to the cloudy weather we had in November and December.
So I downloaded all the insolation data from that weather station and calculated monthly averages for 2007-2014, and used that to estimate how much more or less solar radiation we received in each month of 2014 relative to the average of the prior seven years.
Solar-Insolation-History.gif
This estimate is fairly rough because (a) I only had seven years' of data for calculating the historical average (20-30 years is more typical for calculating climactic averages), (b) the weather station is 15 miles away so may have gotten somewhat different weather, and (c) I didn't even attempt to account for any time of day effects or anything more granular than the monthly average insolation.
But it did give me a way to quantify how much cloudier/sunnier each month was relative to the longer term average. So I used that to adjust the installer's original estimate, by just multiplying the production estimate by the percentage of average radiation received. So for example, in October when we received 112% of the average radiation, I multiplied the installer's estimate of 352 KWh by 112% to get an adjusted estimate of 395 KWh.
Solar-Analysis-3.gif
What Does It Mean?
Four months' of data isn't enough to draw any conclusions, so this is really more a fun exercise in data analysis than any kind of realistic assessment of how accurate my installer's estimate was. Nevertheless, here's a summary:
Month | Installer's Estimate (KWh) | Installer's Estimate Adjusted for Weather (KWh) | Actual Production (KWh) | Est. Production with No Snow (KWh) | "No Snow" Production / Adjusted Installer's Estimate (%) |
September | 548 | 502 | 654 | 654 | 130% |
October | 352 | 395 | 519 | 519 | 131% |
November | 198 | 178 | 161 | 212 | 119% |
December | 119 | 85 | 93 | 114 | 134% |
Comment