Open‐source photovoltaic model pipeline validation against well‐characterized system data Article Swipe
Lelia Deville
,
Marios Theristis
,
Bruce H. King
,
Terrence L. Chambers
,
Joshua S. Stein
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1002/pip.3763
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1002/pip.3763
All freely available plane‐of‐array (POA) transposition models and photovoltaic (PV) temperature and performance models in pvlib‐python and pvpltools‐python were examined against multiyear field data from Albuquerque, New Mexico. The data include different PV systems composed of crystalline silicon modules that vary in cell type, module construction, and materials. These systems have been characterized via IEC 61853‐1 and 61853‐2 testing, and the input data for each model were sourced from these system‐specific test results, rather than considering any generic input data (e.g., manufacturer's specification [spec] sheets or generic Panneau Solaire [PAN] files). Six POA transposition models, 7 temperature models, and 12 performance models are included in this comparative analysis. These freely available models were proven effective across many different types of technologies. The POA transposition models exhibited average normalized mean bias errors (NMBEs) within ±3%. Most PV temperature models underestimated temperature exhibiting mean and median residuals ranging from −6.5°C to 2.7°C; all temperature models saw a reduction in root mean square error when using transient assumptions over steady state. The performance models demonstrated similar behavior with a first and third interquartile NMBEs within ±4.2% and an overall average NMBE within ±2.3%. Although differences among models were observed at different times of the day/year, this study shows that the availability of system‐specific input data is more important than model selection. For example, using spec sheet or generic PAN file data with a complex PV performance model does not guarantee a better accuracy than a simpler PV performance model that uses system‐specific data.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1002/pip.3763
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/pip.3763
- OA Status
- hybrid
- Cited By
- 16
- References
- 23
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4390056855
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4390056855Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1002/pip.3763Digital Object Identifier
- Title
-
Open‐source photovoltaic model pipeline validation against well‐characterized system dataWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-12-20Full publication date if available
- Authors
-
Lelia Deville, Marios Theristis, Bruce H. King, Terrence L. Chambers, Joshua S. SteinList of authors in order
- Landing page
-
https://doi.org/10.1002/pip.3763Publisher landing page
- PDF URL
-
https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/pip.3763Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/pip.3763Direct OA link when available
- Concepts
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Photovoltaic system, Spec#, Python (programming language), Computer science, Root mean square, Mean squared error, Reliability engineering, Simulation, Statistics, Engineering, Mathematics, Programming language, Electrical engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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16Total citation count in OpenAlex
- Citations by year (recent)
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2025: 8, 2024: 8Per-year citation counts (last 5 years)
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23Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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