The core mission of pvlib-python is to provide open, reliable, interoperable, and benchmark implementations of PV system models.
There are at least as many opinions about how to model PV systems as there are modelers of PV systems, so pvlib-python provides several modeling paradigms: functions, the Location/PVSystem classes, and the ModelChain class. Read more about this in the Intro Examples section.
There are many other ways to organize PV modeling code. We encourage you to build on these paradigms and to share your experiences with the pvlib community via issues and pull requests.
The pvlib-python google group is used for discussing various topics of interest to the pvlib-python community. We also make new version announcements on the google group.
If you suspect that you may have discovered a bug or if you’d like to change something about pvlib, then please make an issue on our GitHub issues page .
How do I contribute?¶
We’re so glad you asked! Please see Contributing for information and instructions on how to contribute. We really appreciate it!
The pvlib-python community thanks Sandia National Lab for developing PVLIB Matlab and for supporting Rob Andrews of Calama Consulting to port the library to Python. Will Holmgren thanks the Department of Energy’s Energy Efficiency and Renewable Energy Postdoctoral Fellowship Program (2014-2016), the University of Arizona Institute for Energy Solutions (2017-2018), and the DOE Solar Forecasting 2 program (2018). The pvlib-python maintainers thank all of pvlib’s contributors of issues and especially pull requests. The pvlib-python community thanks all of the maintainers and contributors to the PyData stack.