Gas station without pumps

2016 October 25

Solar power annual cycle

Filed under: Uncategorized — gasstationwithoutpumps @ 16:25
Tags: ,

I’ve been running my solar panels for almost 15 months now, and I decided to plot the daily energy production:

The sinusoidal fits are to all the data (the lower curve), and to the data after cleaning up the bad-weather drops in energy.

The sinusoidal fits are to all the data (the lower curve), and to the data after cleaning up the bad-weather drops in energy.

When I first looked at the data, I realized that a sinusoidal fit to the data would be heavily affected by the cloudy days, which drop power production substantially, so I wrote a script to delete any day whose power was less than the day on either side. Doing three passes of that resulted in most of the really low values being gone, so I fitted a sinusoid to that subset of the data—what I would have gotten if there were no bad-weather days.

When using all the data, I averaged about 7.1 kWh a day, with a ±3.4 kWh annual fluctuation. With just the good-weather data, I averaged about 8.1 kWh a day, with a ±3.5kWh annual fluctuation. That means that I’m losing about 1/8 of the potential solar capacity to cloudy weather. The max and min are within one day of the solstices, so the fitting is doing a pretty good job of finding the phase.

The fluctuation is not perfectly fit by a sine wave, though, as the summer peak is a bit broader than the winter valley. If I were ambitious, I would try seeing how well the data fits with the equation of time or the sunrise equation, instead of a simple sinusoid. I don’t think that would actually help the fit much, as I think that the biggest part of the error is due to shadowing by trees or buildings in the early morning and late afternoon, and this shadowing is more pronounced with the low sun angles of winter.

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2 Comments »

  1. You know, it’s almost as if the days are longer and the sunlight is more perpendicular to the panels in the summer.

    ;-)

    You really should wrap the data on top of the previous year. Even though counting from an equinox would be better for your fit, you might plot the day of the calendar year to make it easy to change symbols for each year. Plotting one year over another would show if any of the aparent anomalies (like the dip I notice around mid to late June) are due to that year’s weather, but it would also make it easier to estimate the max output on a given day. Max output seems like the thing you should be trying to fit, because it will allow you to forecast your “income” and also expose any drop in efficiency as the equipment gets older.

    Comment by CCPhysicist — 2016 October 29 @ 14:52 | Reply

    • The upper fit is an approximation to “max output”, based on dropping the data that is lower than neighboring data. I was thinking of superimposing different years, but with only 15 months I thought that might be a bit misleading—when I get up to 2 full years I’ll probably start doing that.

      Comment by gasstationwithoutpumps — 2016 October 29 @ 15:58 | Reply


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