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The windpowerlib is a library to model the output of wind turbines and farms.

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At the dev meeting we decided that it would be useful to provide a function to check provided power and power coefficient curves for plausibility. We can gather ideas on...

enhancement

We decided that we wanted to add a list of new features/changes that we have in mind but don't have the time to implement to the documentation as an inspiration...

help wanted
nice-to-have

Weather data in the ModelChain currently needs to be a MultiIndex DataFrame. This has on the one hand the drawback that the data height can only be a scalar and...

nice-to-have

In my opinion the way kwargs is used in this method is confusing. I think we should revise the concept. Or maybe I just do not understand it.

question

in [PR53](https://github.com/wind-python/windpowerlib/pull/53) properties were added. To have them appear in the documentation I added rst files for WindFarm and WindTurbineCluster documentation. Locally the build works as expected, but on rtd...

enhancement

@SabineHaas As mentioned before, I tested the windpowerlib to find out whether the feedin exceeds the nominal power of a turbine. I used the following script: https://gist.github.com/nesnoj/b275925d829cd03fce871e2e9d825490 * The max....

The output of wind farms is quite long and people may miss the rest of error messages because of that. This should be changed for all error messages in the...

It would be very good to have the latest version on pypi to not rely on installations from github source directly

Hi, Thanks for creating the lib! I am wondering whether the wind orientation is considered in the calculation. If not what is the assumption of wind direction? Like always face...

File /srv/conda/envs/notebook/lib/python3.10/site-packages/windpowerlib/power_curves.py:143, in smooth_power_curve(power_curve_wind_speeds, power_curve_values, block_width, wind_speed_range, standard_deviation_method, mean_gauss, **kwargs) 141 maximum_value = power_curve_wind_speeds.iloc[-1] + wind_speed_range 142 while power_curve_wind_speeds.values[-1] < maximum_value: --> 143 power_curve_wind_speeds = power_curve_wind_speeds.append( 144 pd.Series( 145 power_curve_wind_speeds.iloc[-1]...