OpenOA Operational Analysis Framework

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This library provides a generic framework for working with large timeseries data from wind plants. Its development has been motivated by the WP3 Benchmarking (PRUF) project, which aims to provide a reference implementaiton for plant-level performance assessment.

The implementation makes use of a flexible backend, so that data loading, processing, and analysis can be performed locally (e.g., with Pandas dataframes), in a semi-distributed manner (e.g., with Dask dataframes), or in a fully distributed matter (e.g., with Spark dataframes).

Data processing and ETL is handled by the PlantData class and by project-specific modules which implement subclasses. These modules can be used to import, inspect, pre-process, and save the raw data from wind turbines, meters, met towers, and reanalysis products such as Merra2.

Analysis routines are grouped by purpose into toolkits - which provide an abstract low level API for common computations, and methods - which provide higher level wind industry specific API. In addition to these provided modules, anyone can write their own, which is intended to provide natural growth of tools within this framework.

To interact with how each of these components of OpenOA are used, please visit our examples notebooks on Binder, or view them statically on the examples page.

If you use this software in your work, please cite our JOSS article with the following BibTex:

    doi = {10.21105/joss.02171},
    url = {},
    year = {2021},
    publisher = {The Open Journal},
    volume = {6},
    number = {58},
    pages = {2171},
    author = {Jordan Perr-Sauer and Mike Optis and Jason M. Fields and Nicola Bodini and Joseph C.Y. Lee and Austin Todd and Eric Simley and Robert Hammond and Caleb Phillips and Monte Lunacek and Travis Kemper and Lindy Williams and Anna Craig and Nathan Agarwal and Shawn Sheng and John Meissner},
    title = {OpenOA: An Open-Source Codebase For Operational Analysis of Wind Farms},
    journal = {Journal of Open Source Software}