Tuning up for the performance

Last night rather unexpectedly my solcast solar irradiance data tuned itself. I use this data to predict the output of my solar panels and adjust what I buy from the grid in response. I had expected that tuning would happen eventually, but my understanding was that two month’s data was required, not the two week’s data that I had so far supplied.

Prior to tuning my predictions had looked like this..

solcast predictions before tuning

Although the system is clearly predicting output to a reasonable degree of accuracy, there are two obvious issues:

  1. The orientation of the array from due south seems a little off, as my array starts and ends generation earlier than the prediction suggesting the the orientation should be slightly more easterly in the model.
  2. The peak at a sustained 4 kW is overly optimistic. The panels can generate 4 kW according to my monitoring, but only relatively briefly and certainly not for more than an hour in March.

Nevertheless, the overall identification of better and worse days is clearly working.

solcast predictions after tuning

However after tuning both issues have been resolved. The measured and predicted curves match very closely. Note that the prior predictions have been updated by the tuning process, so March 26th which is included in both the images looks subtly different. Note also that my control is based on forecasts from the evening before, not the ‘1 hour ahead forecasts’ illustrated above. You may also observe that there is a discrepancy in the morning of April first, where the measured data is zero while the estimated actuals and forecasts are quite healthy, which arises as a result of a temporary failure of the immersun server from where the data is taken.

The ability to do tuning requires an upload of data. I upload generation data continuously at 5-minute intervals (the shortest allowed interval) which may explain the early availability of the tuned results. The script that I use to achieve this takes data indirectly from my ImmerSUN and is modified from this script. I achieved a 99% correlation which is pretty good. Subsequently it seems that the tuning takes place automatically each day.

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