The Baseline Performance Reference for Irradiance in PV System Applications

Driesse, Anton; Habte, Aron; Sengupta, Manajit

doi:10.2172/2000739

Cited by 1 publication

(1 citation statement)

References 4 publications

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“…The performance models compared in this study were taken from two different python packages. SAPM [19], PVWatts v5 ( PVW ) [26], CEC [27], Desoto ( DES ) [28], and PVSyst v6 ( PVS ) were run using pvlib‐python v0.9.3, and ADR [4], Heydenreich ( HEY ) [29], MotherPV ( MOT ) [30], PVGIS ( PVG ) [31], MPM5 [32], MPM6 [32], and Bilinear Interpolation ( BIL ) were run using pvpltools‐python [33]. Table 3 shows the inputs and outputs of each model and groups them by similarity.…”

Section: Overview Of Modelsmentioning

confidence: 99%

Open‐source photovoltaic model pipeline validation against well‐characterized system data

Deville,

Theristis,

King

et al. 2023

Progress in Photovoltaics

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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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