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,"score":8.618846845142738,"created_at":"2026-01-22T00:00:30.085Z","updated_at":"2026-04-07T13:01:08.098Z","avatar_url":"https://github.com/NatLabRockies.png","language":"Python","category":"Renewable Energy","sub_category":"Photovoltaics and Solar Energy","monthly_downloads":0,"total_dependent_repos":0,"total_dependent_packages":0,"readme":"\u003cimg src=\"./docs/sphinx/source/_images/logo_horizontal_highres.png\" width=\"500\" alt=\"RdTools logo\"/\u003e\n\nMaster branch:\n[![Build Status](https://github.com/NREL/rdtools/workflows/pytest/badge.svg?branch=master)](https://github.com/NREL/rdtools/actions?query=branch%3Amaster)\n\nDevelopment branch:\n[![Build Status](https://github.com/NREL/rdtools/workflows/pytest/badge.svg?branch=development)](https://github.com/NREL/rdtools/actions?query=branch%3Adevelopment)\n\nCode coverage:\n[![codecov](https://codecov.io/gh/NREL/rdtools/graph/badge.svg?token=K2HDjFkBws)](https://codecov.io/gh/NREL/rdtools)\n\nRdTools is an open-source library to support reproducible technical analysis of\ntime series data from photovoltaic energy systems. The library aims to provide\nbest practice analysis routines along with the building blocks for users to\ntailor their own analyses. Current applications include the evaluation of PV\nproduction over several years to obtain rates of performance degradation and\nsoiling loss. RdTools can handle both high frequency (hourly or better) or low\nfrequency (daily, weekly, etc.) datasets. Best results are obtained with higher\nfrequency data.\n\nRdTools can be installed automatically into Python from PyPI using the\ncommand line:\n\n```\npip install rdtools\n```\n\nFor API documentation and full examples, please see the [documentation](https://rdtools.readthedocs.io).\n\nRdTools currently is tested on Python 3.9+.\n\n## Citing RdTools\n\nTo cite RdTools, please use the following along with the version number\nand the specific DOI coresponding to that version from [Zenodo](https://doi.org/10.5281/zenodo.1210316):\n\n-  Michael G. Deceglie, Kevin Anderson,  Adam Shinn, Ambarish Nag, Mark Mikofski,\n   Martin Springer, Jiyang Yan, Kirsten Perry, Sandra Villamar, Will Vining,\n   Gregory Kimball, Daniel Ruth, Noah Moyer, Quyen Nguyen, Dirk Jordan,\n   Matthew Muller, and Chris Deline, RdTools, version {insert version}, Computer Software,\n   https://github.com/NREL/rdtools. DOI:{insert DOI}\n\nThe underlying workflow of RdTools has been published in several places.\nIf you use RdTools in a published work, you may also wish to cite the following as\nappropriate:\n\n-  Dirk Jordan, Chris Deline, Sarah Kurtz, Gregory Kimball, Michael Anderson, \"Robust PV\n   Degradation Methodology and Application\", IEEE Journal of\n   Photovoltaics, 8(2) pp. 525-531, 2018, DOI: [10.1109/JPHOTOV.2017.2779779](https://doi.org/10.1109/JPHOTOV.2017.2779779)\n\n-  Michael G. Deceglie, Leonardo Micheli and Matthew Muller, \"Quantifying Soiling Loss\n   Directly From PV Yield,\" in IEEE Journal of Photovoltaics, 8(2),\n   pp. 547-551, 2018, DOI: [10.1109/JPHOTOV.2017.2784682](https://doi.org/10.1109/JPHOTOV.2017.2784682)\n\n-  Kevin Anderson and Ryan Blumenthal, \"Overcoming Communications Outages in\n   Inverter Downtime Analysis\", 2020 IEEE 47th Photovoltaic Specialists\n   Conference (PVSC)\" DOI: [10.1109/PVSC45281.2020.9300635](https://doi.org/10.1109/PVSC45281.2020.9300635)\n\n-  Kirsten Perry, Matthew Muller and Kevin Anderson, \"Performance Comparison of Clipping\n   Detection Techniques in AC Power Time Series,\" 2021 IEEE 48th Photovoltaic\n   Specialists Conference (PVSC), pp. 1638-1643 2021, DOI: [10.1109/PVSC43889.2021.9518733](https://doi.org/10.1109/PVSC43889.2021.9518733).\n\n\n## References\nThe clear sky temperature calculation, `clearsky_temperature.get_clearsky_tamb()`, uses data\nfrom images created by Jesse Allen, NASA’s Earth Observatory using data courtesy of the MODIS Land Group.\nhttps://neo.sci.gsfc.nasa.gov/view.php?datasetId=MOD_LSTD_CLIM_M\nhttps://neo.sci.gsfc.nasa.gov/view.php?datasetId=MOD_LSTN_CLIM_M\n\nOther useful references which may also be consulted for degradation rate methodology include:\n\n  - D. C. Jordan, M. G. Deceglie, S. R. Kurtz, \"PV degradation methodology comparison — A basis for a standard\", in 43rd IEEE Photovoltaic Specialists Conference, Portland, OR, USA, 2016, DOI: 10.1109/PVSC.2016.7749593.\n  - Jordan DC, Kurtz SR, VanSant KT, Newmiller J, Compendium of Photovoltaic Degradation Rates, Progress in Photovoltaics: Research and Application, 2016, 24(7), 978 - 989.\n  - D. Jordan, S. Kurtz, PV Degradation Rates – an Analytical Review, Progress in Photovoltaics: Research and Application, 2013, 21(1), 12 - 29.\n  - E. Hasselbrink, M. Anderson, Z. Defreitas, M. Mikofski, Y.-C.Shen, S. Caldwell, A. Terao, D. Kavulak, Z. Campeau, D. DeGraaff, \"Validation of the PVLife model using 3 million module-years of live site data\", 39th IEEE Photovoltaic Specialists Conference, Tampa, FL, USA, 2013, p. 7 – 13, DOI: 10.1109/PVSC.2013.6744087.\n\n## Further Instructions and Updates\n\nCheck out the [wiki](https://github.com/NREL/rdtools/wiki) for additional usage documentation, and for information on development goals and framework.\n\n","funding_links":[],"readme_doi_urls":["https://doi.org/10.5281/zenodo.1210316","https://doi.org/10.1109/JPHOTOV.2017.2779779","https://doi.org/10.1109/JPHOTOV.2017.2784682","https://doi.org/10.1109/PVSC45281.2020.9300635","https://doi.org/10.1109/PVSC43889.2021.9518733"],"works":{},"citation_counts":{},"total_citations":0,"keywords_from_contributors":["photovoltaic","renewable-energy","renewables","solar-energy","bifacial","solar","pvlib","pv","solar-power","convolutional-neural-networks"],"project_url":"https://ost.ecosyste.ms/api/v1/projects/342281","html_url":"https://ost.ecosyste.ms/projects/342281"}