https://github.com/NREL/rdtools

PV Analysis Tools in Python
https://github.com/NREL/rdtools

Last synced: 8 days ago
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PV Analysis Tools in Python

• Host: GitHub
• URL: https://github.com/NREL/rdtools
• Owner: NREL
• License: mit
• Created: 2016-11-18T22:17:01.000Z (almost 8 years ago)
• Default Branch: master
• Last Pushed: 2024-10-29T03:36:04.000Z (11 days ago)
• Last Synced: 2024-10-29T19:53:55.861Z (10 days ago)
• Language: Python
• Homepage: https://rdtools.readthedocs.io/
• Size: 77.4 MB
• Stars: 156
• Watchers: 26
• Forks: 67
• Open Issues: 45
• Metadata Files:
  • Readme: README.md
  • Contributing: .github/CONTRIBUTING.md
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.md

Awesome Lists containing this project

• awesome-power-analysis - RdTools - RdTools is a set of Python tools for analysis of photovoltaic data. (Solar Analysis)
• open-sustainable-technology - rdtools - An open source library to support reproducible technical analysis of time series data from photovoltaic energy systems. (Renewable Energy / Photovoltaics and Solar Energy)

README

        


Master branch:

[![Build Status](https://github.com/NREL/rdtools/workflows/pytest/badge.svg?branch=master)](https://github.com/NREL/rdtools/actions?query=branch%3Amaster)

Development branch:

[![Build Status](https://github.com/NREL/rdtools/workflows/pytest/badge.svg?branch=development)](https://github.com/NREL/rdtools/actions?query=branch%3Adevelopment)

Code coverage:

[![codecov](https://codecov.io/gh/NREL/rdtools/graph/badge.svg?token=K2HDjFkBws)](https://codecov.io/gh/NREL/rdtools)

RdTools is an open-source library to support reproducible technical analysis of

time series data from photovoltaic energy systems. The library aims to provide

best practice analysis routines along with the building blocks for users to

tailor their own analyses.

Current applications include the evaluation of PV production over several years to obtain

rates of performance degradation and soiling loss. RdTools can handle

both high frequency (hourly or better) or low frequency (daily, weekly,

etc.) datasets. Best results are obtained with higher frequency data.

RdTools can be installed automatically into Python from PyPI using the

command line:

```

pip install rdtools

```

For API documentation and full examples, please see the [documentation](https://rdtools.readthedocs.io).

RdTools currently is tested on Python 3.7+.

## Citing RdTools

To cite RdTools, please use the following along with the version number

and the specific DOI coresponding to that version from [Zenodo](https://doi.org/10.5281/zenodo.1210316):

-  Michael G. Deceglie, Ambarish Nag, Adam Shinn, Gregory Kimball,

   Daniel Ruth, Dirk Jordan, Jiyang Yan, Kevin Anderson, Kirsten Perry,

   Mark Mikofski, Matthew Muller, Will Vining, and Chris Deline

   RdTools, version {insert version}, Compuer Software,

   https://github.com/NREL/rdtools. DOI:{insert DOI}

The underlying workflow of RdTools has been published in several places.

If you use RdTools in a published work, you may also wish to cite the following as

appropriate:

-  Dirk Jordan, Chris Deline, Sarah Kurtz, Gregory Kimball, Michael Anderson, "Robust PV

   Degradation Methodology and Application", IEEE Journal of

   Photovoltaics, 8(2) pp. 525-531, 2018, DOI: [10.1109/JPHOTOV.2017.2779779](https://doi.org/10.1109/JPHOTOV.2017.2779779)

-  Michael G. Deceglie, Leonardo Micheli and Matthew Muller, "Quantifying Soiling Loss

   Directly From PV Yield," in IEEE Journal of Photovoltaics, 8(2),

   pp. 547-551, 2018, DOI: [10.1109/JPHOTOV.2017.2784682](https://doi.org/10.1109/JPHOTOV.2017.2784682)

-  Kevin Anderson and Ryan Blumenthal, "Overcoming Communications Outages in

   Inverter Downtime Analysis", 2020 IEEE 47th Photovoltaic Specialists

   Conference (PVSC)" DOI: [10.1109/PVSC45281.2020.9300635](https://doi.org/10.1109/PVSC45281.2020.9300635)

-  Kirsten Perry, Matthew Muller and Kevin Anderson, "Performance Comparison of Clipping

   Detection Techniques in AC Power Time Series," 2021 IEEE 48th Photovoltaic

   Specialists Conference (PVSC), pp. 1638-1643 2021, DOI: [10.1109/PVSC43889.2021.9518733](https://doi.org/10.1109/PVSC43889.2021.9518733).

## References

The clear sky temperature calculation, `clearsky_temperature.get_clearsky_tamb()`, uses data

from images created by Jesse Allen, NASA’s Earth Observatory using data courtesy of the MODIS Land Group.

https://neo.sci.gsfc.nasa.gov/view.php?datasetId=MOD_LSTD_CLIM_M

https://neo.sci.gsfc.nasa.gov/view.php?datasetId=MOD_LSTN_CLIM_M

Other useful references which may also be consulted for degradation rate methodology include:

  - 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.

  - Jordan DC, Kurtz SR, VanSant KT, Newmiller J, Compendium of Photovoltaic Degradation Rates, Progress in Photovoltaics: Research and Application, 2016, 24(7), 978 - 989.

  - D. Jordan, S. Kurtz, PV Degradation Rates – an Analytical Review, Progress in Photovoltaics: Research and Application, 2013, 21(1), 12 - 29.

  - 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.

## Further Instructions and Updates

Check out the [wiki](https://github.com/NREL/rdtools/wiki) for additional usage documentation, and for information on development goals and framework.