Skip to content

Commit

Permalink
Merge pull request #5890 from openjournals/joss.06984
Browse files Browse the repository at this point in the history 
Merging automatically
  • Loading branch information
@editorialbot
editorialbot authored Sep 16, 2024
2 parents 0da3909 + 0070fed commit 28166e7
Show file tree
Hide file tree
Showing 3 changed files with 1,030 additions and 0 deletions.
332 changes: 332 additions & 0 deletions joss.06984/10.21105.joss.06984.crossref.xml
View file
Original file line number Diff line number Diff line change
@@ -0,0 +1,332 @@
<?xml version="1.0" encoding="UTF-8"?>
<doi_batch xmlns="http://www.crossref.org/schema/5.3.1"
xmlns:ai="http://www.crossref.org/AccessIndicators.xsd"
xmlns:rel="http://www.crossref.org/relations.xsd"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
version="5.3.1"
xsi:schemaLocation="http://www.crossref.org/schema/5.3.1 http://www.crossref.org/schemas/crossref5.3.1.xsd">
<head>
<doi_batch_id>20240916194853-dc78b5e4ec7b7463937c1e7ced6c4bf918a5749e</doi_batch_id>
<timestamp>20240916194853</timestamp>
<depositor>
<depositor_name>JOSS Admin</depositor_name>
<email_address>[email protected]</email_address>
</depositor>
<registrant>The Open Journal</registrant>
</head>
<body>
<journal>
<journal_metadata>
<full_title>Journal of Open Source Software</full_title>
<abbrev_title>JOSS</abbrev_title>
<issn media_type="electronic">2475-9066</issn>
<doi_data>
<doi>10.21105/joss</doi>
<resource>https://joss.theoj.org</resource>
</doi_data>
</journal_metadata>
<journal_issue>
<publication_date media_type="online">
<month>09</month>
<year>2024</year>
</publication_date>
<journal_volume>
<volume>9</volume>
</journal_volume>
<issue>101</issue>
</journal_issue>
<journal_article publication_type="full_text">
<titles>
<title>SolarSpatialTools: A Python package for spatial solar
energy analyses</title>
</titles>
<contributors>
<person_name sequence="first" contributor_role="author">
<given_name>Joseph</given_name>
<surname>Ranalli</surname>
<ORCID>https://orcid.org/0000-0002-8184-9895</ORCID>
</person_name>
<person_name sequence="additional"
contributor_role="author">
<given_name>William</given_name>
<surname>Hobbs</surname>
<ORCID>https://orcid.org/0000-0002-3443-0848</ORCID>
</person_name>
</contributors>
<publication_date>
<month>09</month>
<day>16</day>
<year>2024</year>
</publication_date>
<pages>
<first_page>6984</first_page>
</pages>
<publisher_item>
<identifier id_type="doi">10.21105/joss.06984</identifier>
</publisher_item>
<ai:program name="AccessIndicators">
<ai:license_ref applies_to="vor">http://creativecommons.org/licenses/by/4.0/</ai:license_ref>
<ai:license_ref applies_to="am">http://creativecommons.org/licenses/by/4.0/</ai:license_ref>
<ai:license_ref applies_to="tdm">http://creativecommons.org/licenses/by/4.0/</ai:license_ref>
</ai:program>
<rel:program>
<rel:related_item>
<rel:description>Software archive</rel:description>
<rel:inter_work_relation relationship-type="references" identifier-type="doi">10.5281/zenodo.13765574</rel:inter_work_relation>
</rel:related_item>
<rel:related_item>
<rel:description>GitHub review issue</rel:description>
<rel:inter_work_relation relationship-type="hasReview" identifier-type="uri">https://github.com/openjournals/joss-reviews/issues/6984</rel:inter_work_relation>
</rel:related_item>
</rel:program>
<doi_data>
<doi>10.21105/joss.06984</doi>
<resource>https://joss.theoj.org/papers/10.21105/joss.06984</resource>
<collection property="text-mining">
<item>
<resource mime_type="application/pdf">https://joss.theoj.org/papers/10.21105/joss.06984.pdf</resource>
</item>
</collection>
</doi_data>
<citation_list>
<citation key="ranalli_cloud_2021">
<article_title>Cloud advection model of solar irradiance
smoothing by spatial aggregation</article_title>
<author>Ranalli</author>
<journal_title>Journal of Renewable and Sustainable
Energy</journal_title>
<issue>3</issue>
<volume>13</volume>
<doi>10.1063/5.0050428</doi>
<cYear>2021</cYear>
<unstructured_citation>Ranalli, J., &amp; Peerlings, E. E.
M. (2021). Cloud advection model of solar irradiance smoothing by
spatial aggregation. Journal of Renewable and Sustainable Energy, 13(3),
033704. https://doi.org/10.1063/5.0050428</unstructured_citation>
</citation>
<citation key="Ranalli2024_JPV">
<article_title>PV Plant Equipment Labels and Layouts Can Be
Validated by Analyzing Cloud Motion in Existing Plant
Measurements</article_title>
<author>Ranalli</author>
<journal_title>IEEE Journal of Photovoltaics</journal_title>
<doi>10.1109/JPHOTOV.2024.3366666</doi>
<cYear>2024</cYear>
<unstructured_citation>Ranalli, J., &amp; Hobbs, W. B.
(2024). PV Plant Equipment Labels and Layouts Can Be Validated by
Analyzing Cloud Motion in Existing Plant Measurements. IEEE Journal of
Photovoltaics.
https://doi.org/10.1109/JPHOTOV.2024.3366666</unstructured_citation>
</citation>
<citation key="Ranalli2024_PVSC">
<article_title>Automating Methods for Validating PV Plant
Equipment Labels</article_title>
<author>Ranalli</author>
<journal_title>52nd IEEE PV Specialists
Conference</journal_title>
<cYear>2024</cYear>
<unstructured_citation>Ranalli, J., &amp; Hobbs, W. B.
(2024, June). Automating Methods for Validating PV Plant Equipment
Labels. 52nd IEEE PV Specialists Conference.</unstructured_citation>
</citation>
<citation key="virtanen_scipy_2020">
<article_title>SciPy 1.0: Fundamental algorithms for
scientific computing in Python</article_title>
<author>Virtanen</author>
<journal_title>Nature Methods</journal_title>
<issue>3</issue>
<volume>17</volume>
<doi>10.1038/s41592-019-0686-2</doi>
<issn>1548-7105</issn>
<cYear>2020</cYear>
<unstructured_citation>Virtanen, P., Gommers, R., Oliphant,
T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson,
P., Weckesser, W., Bright, J., Walt, S. J. van der, Brett, M., Wilson,
J., Millman, K. J., Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R.,
Larson, E., … SciPy 1.0 Contributors. (2020). SciPy 1.0: Fundamental
algorithms for scientific computing in Python. Nature Methods, 17(3),
261–272.
https://doi.org/10.1038/s41592-019-0686-2</unstructured_citation>
</citation>
<citation key="macke_hdcp2_2017">
<article_title>The HD(CP)^{\textrm{2}} Observational
Prototype Experiment (HOPE) – an overview</article_title>
<author>Macke</author>
<journal_title>Atmospheric Chemistry and
Physics</journal_title>
<issue>7</issue>
<volume>17</volume>
<doi>10.5194/acp-17-4887-2017</doi>
<issn>1680-7316</issn>
<cYear>2017</cYear>
<unstructured_citation>Macke, A., Seifert, P., Baars, H.,
Barthlott, C., Beekmans, C., Behrendt, A., Bohn, B., Brueck, M., Bühl,
J., Crewell, S., Damian, T., Deneke, H., Düsing, S., Foth, A., Girolamo,
P. D., Hammann, E., Heinze, R., Hirsikko, A., Kalisch, J., … Xie, X.
(2017). The HD(CP)^{\textrm{2}} Observational Prototype Experiment
(HOPE) – an overview. Atmospheric Chemistry and Physics, 17(7),
4887–4914.
https://doi.org/10.5194/acp-17-4887-2017</unstructured_citation>
</citation>
<citation key="lave_cloud_2013">
<article_title>Cloud speed impact on solar variability
scaling – Application to the wavelet variability model</article_title>
<author>Lave</author>
<journal_title>Solar Energy</journal_title>
<volume>91</volume>
<doi>10.1016/j.solener.2013.01.023</doi>
<issn>0038-092X</issn>
<cYear>2013</cYear>
<unstructured_citation>Lave, M., &amp; Kleissl, J. (2013).
Cloud speed impact on solar variability scaling – Application to the
wavelet variability model. Solar Energy, 91, 11–21.
https://doi.org/10.1016/j.solener.2013.01.023</unstructured_citation>
</citation>
<citation key="marcos_power_2011">
<article_title>Power output fluctuations in large scale pv
plants: One year observations with one second resolution and a derived
analytic model</article_title>
<author>Marcos</author>
<journal_title>Progress in Photovoltaics: Research and
Applications</journal_title>
<issue>2</issue>
<volume>19</volume>
<doi>10.1002/pip.1016</doi>
<issn>1099-159X</issn>
<cYear>2011</cYear>
<unstructured_citation>Marcos, J., Marroyo, L., Lorenzo, E.,
Alvira, D., &amp; Izco, E. (2011). Power output fluctuations in large
scale pv plants: One year observations with one second resolution and a
derived analytic model. Progress in Photovoltaics: Research and
Applications, 19(2), 218–227.
https://doi.org/10.1002/pip.1016</unstructured_citation>
</citation>
<citation key="hoff_quantifying_2010">
<article_title>Quantifying PV power Output
Variability</article_title>
<author>Hoff</author>
<journal_title>Solar Energy</journal_title>
<issue>10</issue>
<volume>84</volume>
<doi>10.1016/j.solener.2010.07.003</doi>
<issn>0038-092X</issn>
<cYear>2010</cYear>
<unstructured_citation>Hoff, T. E., &amp; Perez, R. (2010).
Quantifying PV power Output Variability. Solar Energy, 84(10),
1782–1793.
https://doi.org/10.1016/j.solener.2010.07.003</unstructured_citation>
</citation>
<citation key="pelland_spatiotemporal_2021">
<article_title>Spatiotemporal Interpolation of High
Frequency Irradiance Data for Inverter Testing</article_title>
<author>Pelland</author>
<journal_title>2021 IEEE 48th Photovoltaic Specialists
Conference (PVSC)</journal_title>
<doi>10.1109/PVSC43889.2021.9518827</doi>
<cYear>2021</cYear>
<unstructured_citation>Pelland, S., Gagné, A., Allam, M. A.,
Turcotte, D., &amp; Ninad, N. (2021). Spatiotemporal Interpolation of
High Frequency Irradiance Data for Inverter Testing. 2021 IEEE 48th
Photovoltaic Specialists Conference (PVSC), 0211–0218.
https://doi.org/10.1109/PVSC43889.2021.9518827</unstructured_citation>
</citation>
<citation key="jamaly_robust_2018">
<article_title>Robust cloud motion estimation by
spatio-temporal correlation analysis of irradiance data</article_title>
<author>Jamaly</author>
<journal_title>Solar Energy</journal_title>
<volume>159</volume>
<doi>10.1016/j.solener.2017.10.075</doi>
<issn>0038-092X</issn>
<cYear>2018</cYear>
<unstructured_citation>Jamaly, M., &amp; Kleissl, J. (2018).
Robust cloud motion estimation by spatio-temporal correlation analysis
of irradiance data. Solar Energy, 159, 306–317.
https://doi.org/10.1016/j.solener.2017.10.075</unstructured_citation>
</citation>
<citation key="gagne_directional_2018">
<article_title>Directional Solar Variability
Analysis</article_title>
<author>Gagné</author>
<journal_title>2018 IEEE Electrical Power and Energy
Conference (EPEC)</journal_title>
<doi>10.1109/EPEC.2018.8598442</doi>
<cYear>2018</cYear>
<unstructured_citation>Gagné, A., Ninad, N., Adeyemo, J.,
Turcotte, D., &amp; Wong, S. (2018). Directional Solar Variability
Analysis. 2018 IEEE Electrical Power and Energy Conference (EPEC), 1–6.
https://doi.org/10.1109/EPEC.2018.8598442</unstructured_citation>
</citation>
<citation key="stein_variability_2012">
<article_title>The Variability Index: A New and Novel Metric
for Quantifying Irradiance and PV Output Variability</article_title>
<author>Stein</author>
<journal_title>Proceedings of the World Renewable Energy
Forum</journal_title>
<cYear>2012</cYear>
<unstructured_citation>Stein, J. S., Hansen, C. W., &amp;
Reno, M. J. (2012). The Variability Index: A New and Novel Metric for
Quantifying Irradiance and PV Output Variability. Proceedings of the
World Renewable Energy Forum, 13–17.</unstructured_citation>
</citation>
<citation key="lave_characterizing_2015">
<article_title>Characterizing local high-frequency solar
variability and its impact to distribution studies</article_title>
<author>Lave</author>
<journal_title>Solar Energy</journal_title>
<volume>118</volume>
<doi>10.1016/j.solener.2015.05.028</doi>
<cYear>2015</cYear>
<unstructured_citation>Lave, M., Reno, M. J., &amp;
Broderick, R. J. (2015). Characterizing local high-frequency solar
variability and its impact to distribution studies. Solar Energy, 118,
327–337.
https://doi.org/10.1016/j.solener.2015.05.028</unstructured_citation>
</citation>
<citation key="anderson_pvlib_2023">
<article_title>Pvlib python: 2023 project
update</article_title>
<author>Anderson</author>
<journal_title>Journal of Open Source
Software</journal_title>
<issue>92</issue>
<volume>8</volume>
<doi>10.21105/joss.05994</doi>
<issn>2475-9066</issn>
<cYear>2023</cYear>
<unstructured_citation>Anderson, K. S., Hansen, C. W.,
Holmgren, W. F., Jensen, A. R., Mikofski, M. A., &amp; Driesse, A.
(2023). Pvlib python: 2023 project update. Journal of Open Source
Software, 8(92), 5994.
https://doi.org/10.21105/joss.05994</unstructured_citation>
</citation>
<citation key="andrews_introduction_2014">
<article_title>Introduction to the open source PV LIB for
python Photovoltaic system modelling package</article_title>
<author>Andrews</author>
<journal_title>2014 IEEE 40th Photovoltaic Specialist
Conference (PVSC)</journal_title>
<doi>10.1109/PVSC.2014.6925501</doi>
<cYear>2014</cYear>
<unstructured_citation>Andrews, R. W., Stein, J. S., Hansen,
C., &amp; Riley, D. (2014). Introduction to the open source PV LIB for
python Photovoltaic system modelling package. 2014 IEEE 40th
Photovoltaic Specialist Conference (PVSC), 0170–0174.
https://doi.org/10.1109/PVSC.2014.6925501</unstructured_citation>
</citation>
<citation key="perry_pvanalytics_2022">
<article_title>PVAnalytics: A Python Package for Automated
Processing of Solar Time Series Data</article_title>
<author>Perry</author>
<cYear>2022</cYear>
<unstructured_citation>Perry, K., Vining, W., Anderson, K.,
Muller, M., &amp; Hansen, C. (2022). PVAnalytics: A Python Package for
Automated Processing of Solar Time Series Data (NREL/PR-5K00-83824).
National Renewable Energy Lab. (NREL), Golden, CO (United States).
https://www.osti.gov/biblio/1887283</unstructured_citation>
</citation>
</citation_list>
</journal_article>
</journal>
</body>
</doi_batch>
Binary file added joss.06984/10.21105.joss.06984.pdf
View file
Binary file not shown.
Loading

0 comments on commit 28166e7

Please sign in to comment.