implement transient moving-average thermal model

New Feature Implement the transient moving-average model proposed by Matt Prillman

Describe the solution you’d like a new function pvlib.temperature.prillman(times, ...)

Describe alternatives you’ve considered see also #1080

Additional context see https://github.com/NREL/ssc/issues/261

[jforbess]

I recently became aware of np.nditer, which sped up my rolling window apply greatly. I think you should be able to use it for this exponential weighted function, basically applied as a for loop with an efficiently stored/accessed array. I was doing a simple calc on 20 years of 525600 long series, and it was quite snappy, relative to the rolling window apply which was a long enough process that the full 20 year calc was at least tens of minutes if not an hour. Sorry I don’t have more quantified values.

[cwhanse]

I think we should just require regular sampling.

I don’t see that as a major problem. This function operates on the output of a temperature model (applies a form of exponential smoothing). It is likely that a user calculated that output with a regular time index. Even if not, the smoothing basically justifies interpolation on the input cell temperature and wind speed to a regular index, in the following sense: output from applying the function to interpolated input is likely to be very similar to output when applying the function to the non-regular input then interpolating the output.