The windML framework provides an easy-to-use access to wind data sources within the Python world, building upon numpy, scipy, sklearn, and matplotlib. As a machine learning module, it provides versatile tools for various learning tasks like time-series prediction, classification, clustering, dimensionality reduction, and related tasks.

For an installation guide, an overview of the architecture, and the functionalities of windML, please visit the Getting Started page. For a formal description of the applied techniques, see Techniques. The Examples gallery illustrates the main functionalities.

from windml.datasets.nrel import NREL
from windml.mapping.power_mapping import PowerMapping
from sklearn.neighbors import KNeighborsRegressor
import math

windpark = NREL().get_windpark(NREL.park_id['tehachapi'], 3, 2004, 2005)
target = windpark.get_target()

feature_window, horizon = 3, 3
mapping = PowerMapping()
X = mapping.get_features_park(windpark, feature_window, horizon)
Y = mapping.get_labels_mill(target, feature_window, horizon)
reg = KNeighborsRegressor(10, 'uniform')

train_to, test_to = int(math.floor(len(X) * 0.5)), len(X)
train_step, test_step = 5, 5
reg = reg.fit(X[0:train_to:train_step], Y[0:train_to:train_step])
y_hat = reg.predict(X[train_to:test_to:test_step])

The windML framework is licensed under the three clause BSD License.