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I guess I don't see any reason why an AI needs to provide an accurate prediction of a photovoltaic panels output performance. I thought those things were known variables at time of manufacturer and engineering Aren't these scientifically based principles that we are very familiar with at this point?

I would think it would be for testing new designs or changes to current designs without having to fabricate first.

I guess my point is those things are already relatively not difficult to work out with math? like of course... I doubt anybody is out there developing a solar panel without first running a decent estimation of what they're going to get out of it...

While that is likely the case there are still some occasions where an unexpected change can result in significant improvements. There is just no way to account for all the variables that exist between the math and reality.

The significance of building a ML model to predict output is that you can switch it up and use it to predict the parameters needed for an optimal output. Thus, you could use the model to tell you what materials, layout, and dimensions are needed to make the best theoretical solar cell. That's the time saver

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