Solar Data Tools: a Python library for automated analysis of unlabeled PV data
Effectively processing and leveraging the growing volume of photovoltaic (PV) system performance data is essential for the operation and maintenance of PV systems globally.However, many distributed rooftop PV systems suffer from lower data quality, are difficult to model, and lack access to reliable environmental data.
Authors
- Sara Miskovich (ORCID: https://orcid.org/0000-0002-3302-838X)
- T. Lin (ORCID: https://orcid.org/0009-0002-9102-0201)
- Jonathan Goncalves
- Elpiniki Apostolaki-Iosifidou (ORCID: https://orcid.org/0000-0002-8909-9742)
- Alejandro Londoño‐Hurtado
- Hui Zhang (ORCID: https://orcid.org/0000-0001-8749-5285)
- Jianfei Luo (ORCID: https://orcid.org/0000-0002-0639-8991)
- N. Sudhakar Yadav (ORCID: https://orcid.org/0000-0001-7135-7179)
- Bennet Meyers (ORCID: https://orcid.org/0000-0002-4089-0744)
- David Jose Florez Rodriguez
- Duncan Ragsdale
- Aramis Dufour
- Derin Serbetcioglu
- Ming Xiao (ORCID: https://orcid.org/0000-0001-9072-6371)
- Tadatoshi Takahashi
- Shuai Sheng
- Chengcheng Ding
- Victor-Haoyang Lian
- Jose St Louis
- Nimish Telang
- Mitchell Victoriano
- Claire Berschauer
Institutions
- SLAC National Accelerator Laboratory (US)
- Film Independent (US)
- Carnegie Mellon University (US)
- Stanford University (US)
Publication Details
- Journal
- The Journal of Open Source Software
- Published
- 2025-06-27
- DOI
- https://doi.org/10.21105/joss.08478
- Primary Topic
- Solar Radiation and Photovoltaics
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- U.S. Department of Energy
- Office of Energy Efficiency and Renewable Energy
- Office of Energy Efficiency