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A wind resource data retrieval, visualization, and analysis toolkit.

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albatross 🐦

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A wind resource data retrieval, visualization, and analysis toolkit.

albatross is a Python package designed for wind energy analysis and visualization. It utilizes NREL's Wind Integration National Dataset (WIND) Toolkit and REsource eXtraction (rex) package, and is inspired in part by a similar package aimed instead at Marine Renewable Energy, MHKiT.

Features

albatross consists of two primary modules:

  • requests:
    • Request WIND Toolkit data by lat/lon point via HSDS
    • Read WIND Toolkit data from a local HDF5 file
    • Identify WIND Toolkit region and associated lat/lon coordinates, given a lat/lon point
  • analysis:
    • Draw boxplots for inferred windspeed fields (or other specified fields)
    • Plot windrose chart for wind speed and direction data
    • Fit a Weibull distribution for wind speed data and plot a histogram/line chart showing probability density
    • Generate and/or plot diurnal statistics for wind speed data
    • Determine turbulence standard deviation using the Normal Turbulence model for wind data and a set of archetype wind turbine configurations

Future enhancements:

  • multi-year requests
  • allow use of CSV for analysis module functions
  • further incorporate turbulence model statistics

Documentation

Documentation is hosted at Read the Docs.

To build the documentation locally, run the following after installing development dependencies:

cd docs
make html

Afterwards, open _build/index.html in a browser.

Installation

albatross has not yet been hosted on PyPI, so for now, install from Github:

pip install git+https://github.com/camirmas/albatross

If you'll be using the requests module, you'll also need to configure HSDS.

Usage

After installation, run Jupyter notebook Usage.ipynb for fully detailed examples.

Development

For development, simply clone the repository, install dependencies, and run tests.

git clone git@github.com:camirmas/albatross.git
pip install -r requirements.txt
pytest

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  • Jupyter Notebook 81.3%
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