Add manuscript files for 2023 JOSS publication (#1855)

* move 2018 files to subdirectory

* initial files for 2023 manuscript

* add joss build job

* Apply suggestions from code review (@AdamRJensen)

Co-authored-by: Adam R. Jensen <[email protected]>

* Apply suggestions from code review (@cwhanse)

Co-authored-by: Cliff Hansen <[email protected]>

* typo and core developers

* Apply suggestions from code review (@wholmgren)

Co-authored-by: Will Holmgren <[email protected]>

* 2015 -> 2013

* remove scripts

* Apply suggestions from code review (@adriesse)

Co-authored-by: Anton Driesse <[email protected]>

* order authors by commit count since last paper

https://github.com/pvlib/pvlib-python/graphs/contributors?from=2018-09-07&to=2023-09-18&type=c

* s/foundation in/foundation on/

---------

Co-authored-by: Adam R. Jensen <[email protected]>
Co-authored-by: Cliff Hansen <[email protected]>
Co-authored-by: Will Holmgren <[email protected]>
Co-authored-by: Anton Driesse <[email protected]>
Co-authored-by: Mark Mikofski <[email protected]>

.github/workflows/joss-pdf.yml

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+# temporary job to rebuild the PDF while coauthors review the manuscript.
+# TODO: delete before merging this PR
+
+on: [push]
+
+jobs:
+  paper:
+    runs-on: ubuntu-latest
+    name: Paper Draft
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v3
+      - name: Build draft PDF
+        uses: openjournals/openjournals-draft-action@master
+        with:
+          journal: joss
+          # This should be the path to the paper within your repo.
+          paper-path: paper/paper.md
+      - name: Upload
+        uses: actions/upload-artifact@v1
+        with:
+          name: paper
+          # This is the output path where Pandoc will write the compiled
+          # PDF. Note, this should be the same directory as the input
+          # paper.md
+          path: paper/paper.pdf

paper/2018/paper.bib

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+@inproceedings{Andrews2014,
+	Author = {R. W. Andrews and J. S. Stein and C. Hansen and D. Riley},
+	Booktitle = {2014 IEEE 40th Photovoltaic Specialist Conference (PVSC)},
+	doi = {10.1109/PVSC.2014.6925501},
+	Title = {Introduction to the open source PV LIB for python Photovoltaic system modelling package},
+	Year = {2014}}
+
+@online{DOESF2,
+    author = {Department of Energy},
+    title = {Solar Forecasting 2},
+    year = 2018,
+    url = {https://www.energy.gov/eere/solar/solar-forecasting-2},
+    urldate = {2018-08-02}}
+
+@article{Gagne2017,
+	Author = {Gagne II, David John and McGovern, Amy and Haupt, Sue Ellen and Williams, John K.},
+	Journal = {Solar Energy},
+	Pages = {383- 393},
+	doi = {10.1016/j.solener.2017.04.031},
+	Title = {Evaluation of statistical learning configurations for gridded solar irradiance forecasting},
+	Volume = {150},
+	Year = {2017}}
+
+@inproceedings{Holmgren2015,
+	Author = {W. F. Holmgren and R. W. Andrews and A. T. Lorenzo and J. S. Stein},
+	Booktitle = {2015 IEEE 42nd Photovoltaic Specialist Conference (PVSC)},
+	Month = {June},
+	Pages = {1-5},
+	Title = {PVLIB Python 2015},
+	Year = {2015},
+	doi = {10.1109/PVSC.2015.7356005}}
+
+@inproceedings{Holmgren2016,
+	Author = {W. F. Holmgren and D. G. Groenendyk},
+	Booktitle = {2016 IEEE 43rd Photovoltaic Specialists Conference (PVSC)},
+	Month = {June},
+	Pages = {0972-0975},
+	Title = {An open source solar power forecasting tool using PVLIB-Python},
+	Year = {2016},
+	doi = {10.1109/PVSC.2016.7749755}}
+
+@inproceedings{Holmgren2017,
+	Author = {William F. Holmgren and Antonio T. Lorenzo and Clifford Hansen},
+	Booktitle = {2017 IEEE 44th Photovoltaic Specialists Conference},
+	Title = {A Comparison of PV Power Forecasts Using PVLib-Python},
+	Year = {2017},
+	doi = {10.5281/zenodo.1400857}}
+
+@inproceedings{Holmgren2018,
+	Author = {William F. Holmgren and Clifford W. Hansen and Joshua S. Stein and Mark A. Mikofski},
+	Booktitle = {2018 IEEE 45th Photovoltaic Specialists Conference},
+	Title = {Review of open source tools for PV modeling},
+	Year = {2018},
+	doi = {10.5281/zenodo.1401378}}
+
+@article{Louwen2017,
+	Author = {Atse Louwen and Ruud E.I. Schropp and Wilfried G.J.H.M. van Sark and Andr{\'e} P.C. Faaij},
+	Journal = {Solar Energy},
+	Pages = {1339 - 1353},
+	Title = {Geospatial analysis of the energy yield and environmental footprint of different photovoltaic module technologies},
+	Volume = {155},
+	Year = {2017},
+	doi = {10.1016/j.solener.2017.07.056}}
+
+@inproceedings{Mikofski2016,
+	Author = {M. Mikofski and A. Oumbe and C. Li and B. Bourne},
+	Booktitle = {2016 IEEE 43rd Photovoltaic Specialists Conference (PVSC)},
+	Month = {June},
+	Pages = {1357-1362},
+	Title = {Evaluation and correction of the impact of spectral variation of irradiance on PV performance},
+	Year = {2016},
+	doi = {10.1109/PVSC.2016.7749837}}
+
+@inproceedings{Mikofski2017,
+	Author = {Mark A. Mikofski and Clifford W. Hansen and William F. Holmgren and Gregory M. Kimball},
+	Booktitle = {2017 IEEE 44th Photovoltaic Specialists Conference},
+	Title = {Use of Measured Aerosol Optical Depth and Precipitable Water to Model Clear Sky Irradiance},
+	Year = {2017},
+	doi = {10.5281/zenodo.1403238}}
+
+@article{Polo2016,
+	Author = {J. Polo, S. Garcia-Bouhaben, M. C. Alonso-Garcia},
+	Journal = {Journal of Renewable and Sustainable Energy},
+	Title = {A comparative study of the impact of horizontal-to-tilted solar irradiance conversion in modelling small PV array performance},
+	Year = {2016},
+	doi = {10.1063/1.4964363}}
+
+@misc{pvlibZenodo,
+    author = {pvlib python Contributors},
+    title = {pvlib python},
+    doi = {10.5281/zenodo.1246152},
+    howpublished = {\url{http://doi.org/10.5281/zenodo.1246152}}}
+
+@inproceedings{Stein2012,
+	Author = {J. S. Stein},
+	Booktitle = {2012 38th IEEE Photovoltaic Specialists Conference},
+	Month = {June},
+	Pages = {003048-003052},
+	Title = {The photovoltaic Performance Modeling Collaborative (PVPMC)},
+	Year = {2012},
+	doi = {10.1109/PVSC.2012.6318225}}
+
+@inproceedings{Stein2016,
+    Author = {J. S. Stein and W. F. Holmgren and J. Forbess and C. W. Hansen},
+    Booktitle = {2016 IEEE 43rd Photovoltaic Specialists Conference (PVSC)},
+    Month = {June},
+    Pages = {3425-3430},
+    Title = {PVLIB: Open source photovoltaic performance modeling functions for Matlab and Python},
+    Year = {2016},
+    doi = {10.1109/PVSC.2016.7750303}}

paper/2018/paper.md

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+---
+title: 'pvlib python: a python package for modeling solar energy systems'
+tags:
+  - Python
+  - solar energy
+  - photovoltaics
+  - renewable energy
+authors:
+  - name: William F. Holmgren
+    orcid: 0000-0001-6218-9767
+    affiliation: 1
+  - name: Clifford W. Hansen
+    orcid: 0000-0002-8620-5378
+    affiliation: 2
+  - name: Mark A. Mikofski
+    orcid: 0000-0001-8001-8582
+    affiliation: 3
+affiliations:
+ - name: Department of Hydrology and Atmospheric Sciences, University of Arizona
+   index: 1
+ - name: Sandia National Laboratories
+   index: 2
+ - name: DNV-GL
+   index: 3
+date: 2 August 2018
+bibliography: paper.bib
+---
+
+# Summary
+
+pvlib python is a community-supported open source tool that provides a
+set of functions and classes for simulating the performance of
+photovoltaic energy systems. pvlib python aims to provide reference
+implementations of models relevant to solar energy, including for
+example algorithms for solar position, clear sky irradiance, irradiance
+transposition, DC power, and DC-to-AC power conversion. pvlib python is
+an important component of a growing ecosystem of open source tools for
+solar energy [@Holmgren2018].
+
+pvlib python is developed on GitHub by contributors from academia,
+national laboratories, and private industry. pvlib python is released
+with a BSD 3-clause license allowing permissive use with attribution.
+pvlib python is extensively tested for functional and algorithm
+consistency. Continuous integration services check each pull request on
+multiple platforms and Python versions. The pvlib python API is
+thoroughly documented and detailed tutorials are provided for many
+features. The documentation includes help for installation and
+guidelines for contributions. The documentation is hosted at
+readthedocs.org as of this writing. A Google group and StackOverflow tag
+provide venues for user discussion and help.
+
+The pvlib python API was designed to serve the various needs of the many
+subfields of solar power research and engineering. It is implemented in
+three layers: core functions, the ``Location`` and ``PVSystem`` classes,
+and the ``ModelChain`` class. The core API consists of a collection of
+functions that implement algorithms. These algorithms are typically
+implementations of models described in peer-reviewed publications. The
+functions provide maximum user flexibility, however many of the function
+arguments require an unwieldy number of parameters. The next API level
+contains the ``Location`` and ``PVSystem`` classes. These abstractions
+provide simple methods that wrap the core function API layer. The method
+API simplification is achieved by separating the data that represents
+the object (object attributes) from the data that the object methods
+operate on (method arguments). For example, a ``Location`` is
+represented by a latitude, longitude, elevation, timezone, and name,
+which are ``Location`` object attributes. Then a ``Location`` object
+method operates on a ``datetime`` to get the corresponding solar
+position. The methods combine these data sources when calling the
+function layer, then return the results to the user. The final level of
+API is the ``ModelChain`` class, designed to simplify and standardize
+the process of stitching together the many modeling steps necessary to
+convert a time series of weather data to AC solar power generation,
+given a PV system and a location.
+
+pvlib python was ported from the PVLib MATLAB toolbox in 2014
+[@Stein2012, @Andrews2014]. Efforts to make the project more pythonic
+were undertaken in 2015 [@Holmgren2015]. Additional features continue to
+be added, see, for example [@Stein2016, @Holmgren2016] and the
+documentation's "What's New" section.
+
+pvlib python has been used in numerous studies, for example, of solar
+power forecasting [@Gagne2017, @Holmgren2017], development of solar
+irradiance models [@Polo2016], and estimation of photovoltaic energy
+potential [@Louwen2017]. Mikofski et. al. used pvlib python to study
+the accuracy of clear sky models with different aerosol optical depth
+and precipitable water data sources [@Mikofski2017] and to determine the
+effects of spectral mismatch on different PV devices [@Mikofski2016].
+pvlib python is a foundational piece of an award, "An Open Source
+Evaluation Framework for Solar Forecasting," made under the Department
+of Energy Solar Forecasting 2 program [@DOESF2].
+
+Plans for pvlib python development includes the implementation of new
+and existing models, addition of functionality to assist with
+input/output, and improvements to API consistency.
+
+The source code for each pvlib python version is archived with Zenodo
+[@pvlibZenodo].
+
+# Acknowledgements
+
+The authors acknowledge and thank the code, documentation, and
+discussion contributors to the project.
+
+WH acknowledges support from the Department of Energy's Energy
+Efficiency and Renewable Energy Postdoctoral Fellowship Program
+(2014-2016), Tucson Electric Power, Arizona Public Service, and Public
+Service Company of New Mexico (2016-2018), and University of Arizona
+Institute for Energy Solutions (2017-2018).
+
+CH acknowledges support from the U.S. Department of Energy's Solar
+Energy Technology Office.
+
+WH and CH acknowledge support from the Department of Energy Solar
+Forecasting 2 program.
+
+MM acknowledges support from SunPower Corporation (2016-2017).
+
+Sandia National Laboratories is a multi-mission laboratory managed and
+operated by National Technology and Engineering Solutions of Sandia,
+LLC., a wholly owned subsidiary of Honeywell International, Inc., for
+the U.S. Department of Energy's National Nuclear Security Administration
+under contract DE-NA-0003525.
+
+# References