Skip to content

Commit

Permalink
Merge pull request #4786 from openjournals/joss.05755
Browse files Browse the repository at this point in the history 
Merging automatically
  • Loading branch information
@editorialbot
editorialbot authored Nov 16, 2023
2 parents 2e48845 + 1dc9156 commit b91fb2e
Show file tree
Hide file tree
Showing 3 changed files with 1,000 additions and 0 deletions.
339 changes: 339 additions & 0 deletions joss.05755/10.21105.joss.05755.crossref.xml
View file
Original file line number Diff line number Diff line change
@@ -0,0 +1,339 @@
<?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>20231116T170444-68d5f67ef7febb92d7b6b965d3d76e36b965d0c5</doi_batch_id>
<timestamp>20231116170444</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>11</month>
<year>2023</year>
</publication_date>
<journal_volume>
<volume>8</volume>
</journal_volume>
<issue>91</issue>
</journal_issue>
<journal_article publication_type="full_text">
<titles>
<title>pvOps: a Python package for empirical analysis of
photovoltaic field data</title>
</titles>
<contributors>
<person_name sequence="first" contributor_role="author">
<given_name>Kirk L.</given_name>
<surname>Bonney</surname>
<ORCID>https://orcid.org/0009-0006-2383-1634</ORCID>
</person_name>
<person_name sequence="additional"
contributor_role="author">
<given_name>Thushara</given_name>
<surname>Gunda</surname>
<ORCID>https://orcid.org/0000-0003-1945-4064</ORCID>
</person_name>
<person_name sequence="additional"
contributor_role="author">
<given_name>Michael W.</given_name>
<surname>Hopwood</surname>
<ORCID>https://orcid.org/0000-0001-6190-1767</ORCID>
</person_name>
<person_name sequence="additional"
contributor_role="author">
<given_name>Hector</given_name>
<surname>Mendoza</surname>
<ORCID>https://orcid.org/0009-0007-5812-606X</ORCID>
</person_name>
<person_name sequence="additional"
contributor_role="author">
<given_name>Nicole D.</given_name>
<surname>Jackson</surname>
<ORCID>https://orcid.org/0000-0002-3814-9906</ORCID>
</person_name>
</contributors>
<publication_date>
<month>11</month>
<day>16</day>
<year>2023</year>
</publication_date>
<pages>
<first_page>5755</first_page>
</pages>
<publisher_item>
<identifier id_type="doi">10.21105/joss.05755</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.10126530</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/5755</rel:inter_work_relation>
</rel:related_item>
</rel:program>
<doi_data>
<doi>10.21105/joss.05755</doi>
<resource>https://joss.theoj.org/papers/10.21105/joss.05755</resource>
<collection property="text-mining">
<item>
<resource mime_type="application/pdf">https://joss.theoj.org/papers/10.21105/joss.05755.pdf</resource>
</item>
</collection>
</doi_data>
<citation_list>
<citation key="deceglie2018rdtools">
<article_title>RdTools: An open source python library for PV
degradation analysis</article_title>
<author>Deceglie</author>
<cYear>2018</cYear>
<unstructured_citation>Deceglie, M. G., Jordan, D., Nag, A.,
Deline, C. A., &amp; Shinn, A. (2018). RdTools: An open source python
library for PV degradation analysis. National Renewable Energy
Lab.(NREL), Golden, CO (United States).</unstructured_citation>
</citation>
<citation key="gunda2020machine">
<article_title>A machine learning evaluation of maintenance
records for common failure modes in PV inverters</article_title>
<author>Gunda</author>
<journal_title>IEEE Access</journal_title>
<volume>8</volume>
<doi>10.1109/ACCESS.2020.3039182</doi>
<cYear>2020</cYear>
<unstructured_citation>Gunda, T., Hackett, S., Kraus, L.,
Downs, C., Jones, R., McNalley, C., Bolen, M., &amp; Walker, A. (2020).
A machine learning evaluation of maintenance records for common failure
modes in PV inverters. IEEE Access, 8, 211610–211620.
https://doi.org/10.1109/ACCESS.2020.3039182</unstructured_citation>
</citation>
<citation key="holmgren2018pvlib">
<article_title>Pvlib python: A python package for modeling
solar energy systems</article_title>
<author>Holmgren</author>
<journal_title>Journal of Open Source
Software</journal_title>
<issue>29</issue>
<volume>3</volume>
<doi>10.21105/joss.00884</doi>
<cYear>2018</cYear>
<unstructured_citation>Holmgren, W. F., Hansen, C. W., &amp;
Mikofski, M. A. (2018). Pvlib python: A python package for modeling
solar energy systems. Journal of Open Source Software, 3(29), 884.
https://doi.org/10.21105/joss.00884</unstructured_citation>
</citation>
<citation key="hopwood2020neural">
<article_title>Neural network-based classification of
string-level IV curves from physically-induced failures of photovoltaic
modules</article_title>
<author>Hopwood</author>
<journal_title>IEEE Access</journal_title>
<volume>8</volume>
<doi>10.1109/ACCESS.2020.3021577</doi>
<cYear>2020</cYear>
<unstructured_citation>Hopwood, M. W., Gunda, T., Seigneur,
H., &amp; Walters, J. (2020). Neural network-based classification of
string-level IV curves from physically-induced failures of photovoltaic
modules. IEEE Access, 8, 161480–161487.
https://doi.org/10.1109/ACCESS.2020.3021577</unstructured_citation>
</citation>
<citation key="hopwood2022classification">
<article_title>Classification of photovoltaic failures with
hidden markov modeling, an unsupervised statistical
approach</article_title>
<author>Hopwood</author>
<journal_title>Energies</journal_title>
<issue>14</issue>
<volume>15</volume>
<doi>10.3390/en15145104</doi>
<cYear>2022</cYear>
<unstructured_citation>Hopwood, M. W., Patel, L., &amp;
Gunda, T. (2022). Classification of photovoltaic failures with hidden
markov modeling, an unsupervised statistical approach. Energies, 15(14),
5104. https://doi.org/10.3390/en15145104</unstructured_citation>
</citation>
<citation key="hopwood2022generation">
<article_title>Generation of data-driven expected energy
models for photovoltaic systems</article_title>
<author>Hopwood</author>
<journal_title>Applied Sciences</journal_title>
<issue>4</issue>
<volume>12</volume>
<doi>10.3390/app12041872</doi>
<cYear>2022</cYear>
<unstructured_citation>Hopwood, M. W., &amp; Gunda, T.
(2022). Generation of data-driven expected energy models for
photovoltaic systems. Applied Sciences, 12(4), 1872.
https://doi.org/10.3390/app12041872</unstructured_citation>
</citation>
<citation key="hopwood2022physics">
<article_title>Physics-based method for generating fully
synthetic IV curve training datasets for machine learning classification
of PV failures</article_title>
<author>Hopwood</author>
<journal_title>Energies</journal_title>
<issue>14</issue>
<volume>15</volume>
<doi>10.3390/en15145085</doi>
<cYear>2022</cYear>
<unstructured_citation>Hopwood, M. W., Stein, J. S., Braid,
J. L., &amp; Seigneur, H. P. (2022). Physics-based method for generating
fully synthetic IV curve training datasets for machine learning
classification of PV failures. Energies, 15(14), 5085.
https://doi.org/10.3390/en15145085</unstructured_citation>
</citation>
<citation key="mendoza2021pvops">
<article_title>pvOps: Improving operational assessments
through data fusion</article_title>
<author>Mendoza</author>
<journal_title>2021 IEEE 48th photovoltaic specialists
conference (PVSC)</journal_title>
<doi>10.1109/PVSC43889.2021.9518439</doi>
<cYear>2021</cYear>
<unstructured_citation>Mendoza, H., Hopwood, M., &amp;
Gunda, T. (2021). pvOps: Improving operational assessments through data
fusion. 2021 IEEE 48th Photovoltaic Specialists Conference (PVSC),
0112–0119.
https://doi.org/10.1109/PVSC43889.2021.9518439</unstructured_citation>
</citation>
<citation key="reback2020pandas">
<article_title>Pandas-dev/pandas: pandas</article_title>
<author>The pandas development team</author>
<doi>10.5281/zenodo.3509134</doi>
<cYear>2020</cYear>
<unstructured_citation>The pandas development team. (2020).
Pandas-dev/pandas: pandas (latest). Zenodo.
https://doi.org/10.5281/zenodo.3509134</unstructured_citation>
</citation>
<citation key="pierce2020identifying">
<article_title>Identifying degradation modes of photovoltaic
modules using unsupervised machine learning on electroluminescense
images</article_title>
<author>Pierce</author>
<journal_title>2020 47th IEEE photovoltaic specialists
conference (PVSC)</journal_title>
<doi>10.1109/PVSC45281.2020.9301021</doi>
<cYear>2020</cYear>
<unstructured_citation>Pierce, B. G., Karimi, A. M., Liu,
J., French, R. H., &amp; Braid, J. L. (2020). Identifying degradation
modes of photovoltaic modules using unsupervised machine learning on
electroluminescense images. 2020 47th IEEE Photovoltaic Specialists
Conference (PVSC), 1850–1855.
https://doi.org/10.1109/PVSC45281.2020.9301021</unstructured_citation>
</citation>
<citation key="klise2016performance">
<article_title>Performance monitoring using pecos (v.
0.1)</article_title>
<author>Klise</author>
<doi>10.2172/1734479</doi>
<cYear>2016</cYear>
<unstructured_citation>Klise, K. A., &amp; Stein, J. S.
(2016). Performance monitoring using pecos (v. 0.1). Sandia National
Laboraties. https://doi.org/10.2172/1734479</unstructured_citation>
</citation>
<citation key="plotly2015">
<article_title>Collaborative data science</article_title>
<author>Plotly Technologies Inc.</author>
<cYear>2015</cYear>
<unstructured_citation>Plotly Technologies Inc. (2015).
Collaborative data science. Plotly Technologies Inc.
https://plot.ly</unstructured_citation>
</citation>
<citation key="waskom2021seaborn">
<article_title>Seaborn: Statistical data
visualization</article_title>
<author>Waskom</author>
<journal_title>Journal of Open Source
Software</journal_title>
<issue>60</issue>
<volume>6</volume>
<doi>10.21105/joss.03021</doi>
<cYear>2021</cYear>
<unstructured_citation>Waskom, M. L. (2021). Seaborn:
Statistical data visualization. Journal of Open Source Software, 6(60),
3021. https://doi.org/10.21105/joss.03021</unstructured_citation>
</citation>
<citation key="hunter2007matplotlib">
<article_title>Matplotlib: A 2D graphics
environment</article_title>
<author>Hunter</author>
<journal_title>Computing in Science &amp;
Engineering</journal_title>
<issue>3</issue>
<volume>9</volume>
<doi>10.1109/MCSE.2007.55</doi>
<cYear>2007</cYear>
<unstructured_citation>Hunter, J. D. (2007). Matplotlib: A
2D graphics environment. Computing in Science &amp; Engineering, 9(3),
90–95. https://doi.org/10.1109/MCSE.2007.55</unstructured_citation>
</citation>
<citation key="bird2009nltk">
<volume_title>Natural language processing with
python</volume_title>
<author>Bird</author>
<cYear>2009</cYear>
<unstructured_citation>Bird, S., Klein, E., &amp; Loper, E.
(2009). Natural language processing with python. O’Reilly
Media.</unstructured_citation>
</citation>
<citation key="chollet2015keras">
<article_title>Keras</article_title>
<author>Chollet</author>
<cYear>2015</cYear>
<unstructured_citation>Chollet, F., &amp; others. (2015).
Keras. https://keras.io.</unstructured_citation>
</citation>
<citation key="pedregosa2011sklearn">
<article_title>Scikit-learn: Machine learning in
Python</article_title>
<author>Pedregosa</author>
<journal_title>Journal of Machine Learning
Research</journal_title>
<volume>12</volume>
<cYear>2011</cYear>
<unstructured_citation>Pedregosa, F., Varoquaux, G.,
Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M.,
Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A.,
Cournapeau, D., Brucher, M., Perrot, M., &amp; Duchesnay, E. (2011).
Scikit-learn: Machine learning in Python. Journal of Machine Learning
Research, 12, 2825–2830.</unstructured_citation>
</citation>
<citation key="perry2022pvanalytics">
<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. National Renewable
Energy Lab.(NREL), Golden, CO (United States).</unstructured_citation>
</citation>
</citation_list>
</journal_article>
</journal>
</body>
</doi_batch>
Loading

0 comments on commit b91fb2e

Please sign in to comment.