windml

The importance of wind in smart grids with a large number of renewable energy resources is increasing. With the growing infrastructure of wind turbines and the availability of time-series data with high spatial and temporal resolution, the application of data mining techniques comes into play.

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.

Getting Started

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.

Brief Example

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])

License

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