References
Citing Us
If using this package, please cite us using the following
Bonney et al., (2023). pvOps: a Python package for empirical analysis of photovoltaic field data.
Journal of Open Source Software, 8(91), 5755, https://doi.org/10.21105/joss.05755
In BibTex format:
@article{Bonney2023,
doi = {10.21105/joss.05755},
url = {https://doi.org/10.21105/joss.05755},
year = {2023},
publisher = {The Open Journal},
volume = {8},
number = {91},
pages = {5755},
author = {Kirk L. Bonney and Thushara Gunda and Michael W. Hopwood and Hector Mendoza and Nicole D. Jackson},
title = {pvOps: a Python package for empirical analysis of photovoltaic field data},
journal = {Journal of Open Source Software} }
We also utilize content from other packages. See the NOTICE/ directory on our GitHub!
Additionally, some of our own content comes from published papers. See the following external references.
External references
J.W. Bishop. Computer simulation of the effects of electrical mismatches in photovoltaic cell interconnection circuits. Solar Cells, 25(1):73–89, 1988. URL: https://www.sciencedirect.com/science/article/pii/0379678788900592, doi:https://doi.org/10.1016/0379-6787(88)90059-2.
Michael G Deceglie, Dirk Jordan, Ambarish Nag, Christopher A Deline, and Adam Shinn. Rdtools: an open source python library for pv degradation analysis. Technical Report, National Renewable Energy Lab.(NREL), Golden, CO (United States), 2018.
T. Dierauf, A. Growitz, S. Kurtz, J. L. B. Cruz, E. Riley, and C. Hansen. Weather-corrected performance ratio. 4 2013. URL: https://www.osti.gov/biblio/1078057, doi:10.2172/1078057.
William F Holmgren, Clifford W Hansen, and Mark A Mikofski. Pvlib python: a python package for modeling solar energy systems. Journal of Open Source Software, 3(29):884, 2018. doi:10.21105/joss.00884.
Michael W. Hopwood and Thushara Gunda. Generation of data-driven expected energy models for photovoltaic systems. Applied Sciences, 2022. URL: https://www.mdpi.com/2076-3417/12/4/1872, doi:10.3390/app12041872.
Michael W. Hopwood, Thushara Gunda, Hubert Seigneur, and Joseph Walters. Neural network-based classification of string-level iv curves from physically-induced failures of photovoltaic modules. IEEE Access, 8():161480–161487, 2020. doi:10.1109/ACCESS.2020.3021577.
Michael W. Hopwood, Joshua S. Stein, Jennifer L. Braid, and Hubert P. Seigneur. Physics-based method for generating fully synthetic iv curve training datasets for machine learning classification of pv failures. Energies, 2022. URL: https://www.mdpi.com/1996-1073/15/14/5085, doi:10.3390/en15145085.
Katherine A Klise and Joshua S Stein. Performance monitoring using pecos (v. 0.1). Technical Report, Sandia National Laboraties, 2016. doi:10.2172/1734479.
Hector Mendoza, Michael Hopwood, and Thushara Gunda. Pvops: improving operational assessments through data fusion. In 2021 IEEE 48th Photovoltaic Specialists Conference (PVSC), volume, 0112–0119. 2021. doi:10.1109/PVSC43889.2021.9518439.
Benjamin G Pierce, Ahmad Maroof Karimi, JiQi Liu, Roger H French, and Jennifer L Braid. Identifying degradation modes of photovoltaic modules using unsupervised machine learning on electroluminescense images. In 2020 47th IEEE Photovoltaic Specialists Conference (PVSC), 1850–1855. IEEE, 2020. doi:10.1109/PVSC45281.2020.9301021.