The windpowerlib is a library that provides a set of functions and classes to calculate the power output of wind turbines. It was originally part of the feedinlib (windpower and photovoltaic) but was taken out to build up a community concentrating on wind power models.
For a quick start see the Examples and basic usage section.
Full documentation can be found at readthedocs.
Use the project site of readthedocs to choose the version of the documentation. Go to the download page to download different versions and formats (pdf, html, epub) of the documentation.
If you have a working Python 3 (>= 3.6) environment, use pypi to install the latest windpowerlib version:
pip install windpowerlib
The windpowerlib is designed for Python 3 and tested on Python >= 3.5. We highly recommend to use virtual environments. Please see the installation page of the oemof documentation for complete instructions on how to install python and a virtual environment on your operating system.
To see the plots of the windpowerlib example in the Examples and basic usage section you should install the matplotlib package. Matplotlib can be installed using pip:
pip install matplotlib
The simplest way to run the example notebooks without installing windpowerlib is to click here and open them with Binder.
The basic usage of the windpowerlib is shown in the ModelChain example that is available as jupyter notebook and python script:
To run the example you need example weather that is downloaded automatically and can also be downloaded here:
To run the examples locally you have to install the windpowerlib. To run the notebook you also need to install notebook using pip3. To launch jupyter notebook type jupyter notebook in the terminal.
This will open a browser window. Navigate to the directory containing the notebook to open it. See the jupyter notebook quick start guide for more information on how to install and
how to run jupyter notebooks. In order to reproduce the figures in a notebook you need to install matplotlib.
Further functionalities, like the modelling of wind farms and wind turbine clusters, are shown in the TurbineClusterModelChain example. As the ModelChain example it is available as jupyter notebook and as python script. The weather used in this example is the same as in the ModelChain example.
- TurbineClusterModelChain example (Python script)
- TurbineClusterModelChain example (Jupyter notebook)
You can also look at the examples in the Examples section.
The windpowerlib provides data of many wind turbines but it is also possible to use your own turbine data.
The windpowerlib provides wind turbine data (power curves, hub heights, etc.) for a large set of wind turbines. See Initialize wind turbine in :ref:`examples_section_label` on how to use this data in your simulations.
The dataset is hosted and maintained on the OpenEnergy database (oedb). To update your local files with the latest version of the oedb turbine library you can execute the following in your python console:
from windpowerlib.wind_turbine import load_turbine_data_from_oedb
load_turbine_data_from_oedb()If you find your turbine in the database it is very easy to use it in the windpowerlib
from windpowerlib import WindTurbine
enercon_e126 = {
"turbine_type": "E-126/4200", # turbine type as in register
"hub_height": 135, # in m
}
e126 = WindTurbine(**enercon_e126)We would like to encourage anyone to contribute to the turbine library by adding turbine data or reporting errors in the data. See here for more information on how to contribute.
It is possible to use your own power curve. However, the most sustainable way is to send us the data to be included in the windpowerlib and to be available for all users. This may not be possible in all cases.
Assuming the data files looks like this:
wind,power 0.0,0.0 3.0,39000.0 5.0,270000.0 10.0,2250000.0 15.0,4500000.0 25.0,4500000.0
You can use pandas to read the file and pass it to the turbine dictionary. I you have basic knowledge of pandas it is easy to use any kind of data file.
import pandas as pd
from windpowerlib import WindTurbine, create_power_curve
my_data = pd.read_csv("path/to/my/data/file.csv")
my_turbine_data = {
"nominal_power": 6e6, # in W
"hub_height": 115, # in m
"power_curve": create_power_curve(
wind_speed=my_data["wind"], power=my_data["power"]
),
}
my_turbine = WindTurbine(**my_turbine2)See the modelchain_example for more information.
We are warmly welcoming all who want to contribute to the windpowerlib. If you are interested in wind models and want to help improving the existing model do not hesitate to contact us via github or email ([email protected]).
Clone: https://github.com/wind-python/windpowerlib and install the cloned repository using pip:
pip install -e /path/to/the/repositoryAs the windpowerlib started with contributors from the oemof developer group we use the same developer rules.
How to create a pull request:
- Fork the windpowerlib repository to your own github account.
- Change, add or remove code.
- Commit your changes.
- Create a pull request and describe what you will do and why.
- Wait for approval.
Generally the following steps are required when changing, adding or removing code:
- Add new tests if you have written new functions/classes.
- Add/change the documentation (new feature, API changes ...).
- Add a whatsnew entry and your name to Contributors.
- Check if all tests still work by simply executing pytest in your windpowerlib directory:
pytestWe use the zenodo project to get a DOI for each version. Search zenodo for the right citation of your windpowerlib version.
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