WindKit

A Python Library for Wind Resource Assessment

WindKit is a Python library designed to streamline wind resource assessment workflows. It provides a comprehensive set of tools for working with wind data, from initial data loading and processing to analysis and visualization.

WindKit is suitable for both new and experienced users in the wind energy field, providing a consistent interface for common tasks.

What can you do with WindKit?

WindKit simplifies your workflow by providing a user-friendly interface for:

  • Data Handling: Work with wind climate data, GIS and terrain maps, wind turbines, and associated metadata. WindKit supports common file formats in the wind energy industry.

  • Common Operations: Perform essential calculations like Weibull fitting, wind speed extrapolation, and sector management.

  • Spatial Analysis: Utilize tools for regridding, masking, and clipping spatial data.

  • Visualization: Create interactive plots and maps with Plotly to explore data and present findings.

Built on a Solid Foundation

WindKit is built on top of industry-standard libraries like xarray for multi-dimensional data and GeoPandas for spatial operations. This provides a robust, high-performance library that integrates into the scientific Python ecosystem. WindKit also emphasizes robust metadata for all objects, which improves interoperability and reusability of data sets.

Part of the WAsP Family

WindKit is a stand-alone library developed alongside PyWAsP. This allows users to view and interact with WAsP’s input and output files, analyze results, and build custom workflows in Python.

By providing this package for free, we hope that other tools that read and write these files will use this package, allowing for standardization across the wind resource assessment community.

Tools Using WindKit

  • PyWAsP - a Python interface to WAsP, which is used for wind resource assessment.

  • Wind-validation

  • New European Wind Atlas data APIs

Contact

References

[1]

Rogier Floors, Peter Enevoldsen, Neil Davis, Johan Arnqvist, and Ebba Dellwik. From lidar scans to roughness maps for wind resource modelling in forested areas. Wind Energy Sci., 3(1):353–370, jun 2018. URL: https://www.wind-energ-sci.net/3/353/2018/, doi:10.5194/wes-3-353-2018.