Open data sets for assessing photovoltaic system reliability

Chen, Xin; Li, Baojie; Braid, Jennifer L.; Byford, Brandon; Colvin, Dylan J.; Glaws, Andrew; Jost, Norman; Pierce, Benjamin; Rabade, Salil; Springer, Martin; Jain, Anubhav

doi:10.1016/j.apenergy.2025.126132

Title: Open data sets for assessing photovoltaic system reliability

Journal Article · Wed May 28 20:00:00 EDT 2025 · Applied Energy

DOI: https://doi.org/10.1016/j.apenergy.2025.126132 · OSTI ID:2573901

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Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); University of California, Berkeley, CA (United States)
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Univ. of Central Florida, Cocoa, FL (United States)
National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Case Western Reserve Univ., Cleveland, OH (United States)

Photovoltaic (PV) systems have become a cornerstone of renewable energy strategies, particularly due to the significant reduction in solar power costs over the past decade. However, the long-term reliability of PV installations presents a persistent challenge, requiring the development of advanced monitoring and predictive maintenance strategies. A wide range of data types is used to evaluate the health of PV systems, including environmental conditions, electrical performance, and inspection imagery. These data enable methodologies such as machine learning (ML) models for lifetime prediction and computer vision techniques for defect detection. However, the acquisition of high-quality and comprehensive data is difficult, particularly in terms of long-term consistency and data variety. Publicly available data sets serve as valuable resources for addressing these challenges, but they often suffer from fragmentation and are difficult to access. This paper presents a comprehensive review of existing open-source data sets related to PV degradation, analyzing their features, functionalities, and potential applications. We categorize these data sets based on the specific aspects of PV system information they cover, such as environmental conditions, operational monitoring, image inspection and module materials, and propose relevant tools and ML models for processing them. In addition, we propose practices for future data collection and usage, while also discussing potential directions in data-driven research. Our aim is to enhance data utilization and publication among researchers and industry professionals, promoting a deeper understanding of the role of data in enhancing the performance and durability of PV systems.

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); National Renewable Energy Laboratory (NREL), Golden, CO (United States)

Sponsoring Organization:: US Department of Energy; USDOE Office of Science (SC)

Grant/Contract Number:: AC02-05CH11231; AC36-08GO28308

OSTI ID:: 2573901

Report Number(s):: NREL/JA--2C00-95519

Journal Information:: Applied Energy, Journal Name: Applied Energy Vol. 395; ISSN 0306-2619

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Performance Data from the NIST Photovoltaic Arrays and Weather Station Boyd, Matthew Journal of Research of the National Institute of Standards and Technology, Vol. 122 https://doi.org/10.6028/jres.122.040	journal	November 2017

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