windpowerlib.wake_losses.get_wind_efficiency_curve¶

windpowerlib.wake_losses.get_wind_efficiency_curve(curve_name='all')[source]¶

Reads wind efficiency curve(s) specified in curve_name.

Parameters:	curve_name (str or list) – Specifies the curve. Use ‘all’ to get all curves in a MultiIndex DataFrame or one of the curve names to retrieve a single curve. Default: ‘all’.
Returns:	efficiency_curve – Wind efficiency curve. Contains ‘wind_speed’ and ‘efficiency’ columns with wind speed in m/s and wind efficiency (dimensionless). If curve_name is ‘all’ or a list of strings a MultiIndex DataFrame is returned with curve names in the first level of the columns.
Return type:	pd.DataFrame

Notes

The wind efficiency curves were generated in the “Dena Netzstudie” [1] and in the work of Kaspar Knorr [2]. The mean wind efficiency curve is an average curve from 12 wind farm distributed over Germany ([1]) or respectively an average from over 2000 wind farms in Germany ([2]). Curves with the appendix ‘extreme’ are wind efficiency curves of single wind farms that are extremely deviating from the respective mean wind efficiency curve. For more information see [1] and [2].

References

[1]	(1, 2, 3) Kohler et.al.: “dena-Netzstudie II. Integration erneuerbarer Energien in die deutsche Stromversorgung im Zeitraum 2015 – 2020 mit Ausblick 2025.”, Deutsche Energie-Agentur GmbH (dena), Tech. rept., 2010, p. 101

[2]	(1, 2, 3) Knorr, K.: “Modellierung von raum-zeitlichen Eigenschaften der Windenergieeinspeisung für wetterdatenbasierte Windleistungssimulationen”. Universität Kassel, Diss., 2016, p. 124

Examples

# Example to plot all curves
fig, ax = plt.subplots() /n
df = get_wind_efficiency_curve(curve_name='all')
for t in df.columns.get_level_values(0).unique():
    p = df[t].set_index('wind_speed')['efficiency']
    p.name = t
    ax = p.plot(ax=ax, legend=True)
plt.show()