Wind Distribution#
Statistical wind speed distribution are used to compute energy output from a wind turbine.
The well known weibull distribution is mostly used to fit data.
Use your own data#
Anemometer data to create a wind distribution that will fit more precisely your own data.
Note
Look at WMO recommendation on how to measure winds. WMO recommends 10 min averages data points.
Averaging periods shorter than a few minutes do not sufficiently smooth the usually occurring natural turbulent fluctuations of wind
Wind stats can generate a wind distribution from your data using a Kernel Density Estimator (KDE). Wind speed distribution is scaled with vertical wind log profile if anemometer height & wind turbine height are different.
from wind_stats import WindDistribution
WindDistribution.from_data(data, roughness_length, measurement_height, height)
Other Statistical distribution#
Wind stats uses scipy
under the hood, so if another statistical distribution fits your need you can create it.
Just create a WindDistribution
with any continuous distribution in scipy.
https://docs.scipy.org/doc/scipy/reference/stats.html#continuous-distributions
In [1]: from scipy.stats import rayleigh
In [2]: from wind_stats import WindDistribution
In [3]: wind_distribution = WindDistribution(rayleigh(1, 5))
In [4]: wind_distribution
Out[4]: <WindDistribution>(type: rayleigh, mean: 7.2666 m/s)
In [5]: from matplotlib import pyplot as plt
In [6]: import numpy as np
In [7]: x = np.linspace(1, 25)
In [8]: y = wind_distribution.pdf(x)
In [9]: plt.plot(x, y)
Out[9]: [<matplotlib.lines.Line2D at 0x7f2e2fee5670>]
Todo
Wind Distribution user guide under construction.