The windML framework provides an easy-to-use access to wind data sources within the Python world, building upon numpy, scipy, sklearn, and matplotlib. Renewable Wind Energy, Forecasting, Prediction
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Python 2 and 3 compatibility and dependencies update
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README.md

windml

Build status : build passing

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.

Install

Using pip: pip install git+https://github.com/cigroup-ol/windml.git.

The basemap is tricky to install unless you are using conda (conda install basemap). Otherwise you should install from source e.g. : pip install https://github.com/matplotlib/basemap/archive/v1.0.7rel.tar.gz.

pkgconfig, freetype and libpng are necessary to build the package from source (matplotlib install depends on it). The requirements.txt file is purely cosmetic as scikit-learn requires scipy (and numpy) to be preinstalled and more importantly there is no guarantee that scipy will be installed prior to scikit-learn.

  • MacOS:
brew install pkg-config
brew install freetype
brew install libpng