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CHANGELOG.md

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Changelog

All notable changes to this project will be documented in this file. If you make a notable change to the project, please add a line describing the change to the "unreleased" section. The maintainers will make an effort to keep the Github Releases page up to date with this changelog. The format is based on Keep a Changelog.

[2.3 - 2022-01-18]

  • Replaced hard-coded reanalysis dates in plant analysis with automatic valid date selection and added optional user-defined end date argument. Fixed bug in normalization to 30-day months.
  • Toolkit added for downloading reanalysis data using the PlanetOS API
  • Added hourly resolution to AEP calculation
  • Added wind farm plotting function to pandas_plotting toolkit using the Bokeh library
  • Split the QC methods into a more generic WindToolKitQualityControlDiagnosticSuite class and WTK-specific subclass: WindToolKitQualityControlDiagnosticSuite.
  • Updated filter algorithms in AEP calculation, now with a proper outlier filter

[2.2 - 2021-05-28]

  • IAV incorporation in AEP calculation
  • Set power to 0 for windspeeds above and below cutoff in IEC power curve function.
  • Split unit tests from regression tests and updated CI pipeline to run the full regression tests weekly.
  • Flake8 with Black code style implemented with git hook to run on commit
  • Updated long-term loss calculations to weight by monthly/daily long-term gross energy
  • Added wind turbine asset data to example ENGIE project
  • Reduce amount of time it takes to run regression tests by decreasing number of monte carlo iterations. Reduce tolerance of float comparisons in plant analysis regression test. Linear regression on daily data is removed from test.
  • Bugfixes, such as fixing an improper python version specifier in setup.py and replacing some straggling references to the master branch with main.

[2.1 - 2021-02-17]

  • Modify bootstrapping approach for period of record sampling. Data is now sampled with replacement, across 100% of the POR data.
  • Cleaned up dependencies for JOSS review. Adding peer-reviewed JOSS paper.
  • Add Binder button to Readme which makes running the example notebooks easier.
  • Set maximum python version to 3.8, due to an install issue for dependency Shapely on Mac with Python 3.9.

[2.0.1 - 2020-10-13]

  • Replaced GeoPandas functionality with pyproj and Shapely for coordinate reference system conversion and distance measurements.
  • Moved and renamed tests and updated the documentation accordingly.

[2.0.0 - 2020-08-11]

  • Switch to semantic versioning from this release forward.
  • Efficiency improvements in AEP calculation
  • Energy Yield Analysis (EYA) added to Operational Assessment (OA) Gap Analysis method
  • Uncertainty quantification for electrical losses and longterm turbine gross energy
  • Implemented open source Engie example data
  • Complete update of example notebooks
  • Switch to standard BSD-3 Clause license
  • Automated quality control method to assist with data ingestion. Tools in this method include daylight savings time change detection and identification of the diurnal cycle.
  • Add electrical losses method
  • Method for estimating long-term turbine gross energy (excluding downtime and underperformance losses)
  • CI pipeline using Github Actions includes regression testing with Pytest, code coverage reporting via CodeCov, packaging and distribution via Pypi, and automatic documentation using ReadTheDocs.

[1.1] - 2019-01-29

  • Python3 Support
  • Addition of reanalysis schemas to the Sphinx documentation
  • Easy import of EIA data using new module: Metadata_Fetch
  • Updated contributing.md document
  • Quality checks for reanalysis data
  • Improved installation instructions
  • Integration tests are now performed in CI
  • Performed PEP8 linting

[1.0] - 2018-12-06

  • Refactor many analysis and toolkit modules to make them conform to a standard API (init, prepare, and run method).
  • Timeseries Table is now an integrated component, no sparkplug-datastructures dependency
  • Plant Level AEP method w/ Monte Carlo
  • Turbine / Scada level toolkits: Filtering, Imputing, Met, Pandas Plotting, Timeseries, Unit Conversion
  • Most toolkits and all methods are fully documented in Sphinx.
  • Two example notebooks: Operational AEP Analysis and Turbine Analysis
  • All toolkits except for Pandas Plotting have > 80% test coverage.