Open‐source photovoltaic model pipeline validation against well‐characterized system data
[1];
[2];
- Department of Photovoltaics and Materials Technology Sandia National Laboratories Albuquerque New Mexico 87185 USA, Department of Mechanical Engineering University of Louisiana at Lafayette Lafayette Louisiana 70504 USA
- Department of Photovoltaics and Materials Technology Sandia National Laboratories Albuquerque New Mexico 87185 USA
- Department of Mechanical Engineering University of Louisiana at Lafayette Lafayette Louisiana 70504 USA
Abstract 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.
- Research Organization:
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office
- Grant/Contract Number:
- NA0003525
- OSTI ID:
- 2248120
- Alternate ID(s):
- OSTI ID: 2248121; OSTI ID: 2311424
- Report Number(s):
- SAND-2024-00266J
- Journal Information:
- Progress in Photovoltaics, Journal Name: Progress in Photovoltaics Vol. 32 Journal Issue: 5; ISSN 1062-7995
- Publisher:
- Wiley Blackwell (John Wiley & Sons)Copyright Statement
- Country of Publication:
- United Kingdom
- Language:
- English
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