Comparison of influence of gold contamination on the performances of planar and three dimensional c-Si solar cells

Baytemir, Gülsen; Es, Firat; Turan, Raşit

doi:10.1016/j.renene.2019.04.081

Cited by 5 publications

(2 citation statements)

References 30 publications

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“…The relatively simple clear‐sky method could be improved by either replacing it with a different method (e.g., using satellite data with clear‐sky filtering [ 32 ] ), or by applying some site‐adaptation techniques to remove bias. The existing clear‐sky method appears to be dependent on climate and fails completely at locations characterized by more dynamic conditions.…”

Section: Rdtools Potential Improvement Recommendationsmentioning

confidence: 99%

“…Periodic calibrations and maintenance are recommended for ground‐based sensors. When ground‐based measurements are unreliable, satellite data in combination with clear‐sky filtering could be leveraged to avoid any bias due to irradiance sensor drifting [ 32 ] (although the uncertainty of the satellite data must be considered as well); this is being incorporated in RdTools. Nighttime “irradiance” measurements can provide useful information on the calibration of a particular sensor.…”

Section: Climate and Data Quality Sensitivity Of The Rdtools Yoy Appr...mentioning

confidence: 99%

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How Climate and Data Quality Impact Photovoltaic Performance Loss Rate Estimations

Theristis,

Anderson,

Ascencio-Vasquez

et al. 2023

Solar RRL

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Different data pipelines and statistical methods are applied to photovoltaic (PV) performance datasets to quantify the performance loss rate (PLR). Since the real values of PLR are unknown, a variety of unvalidated values are reported. As such, the PV industry commonly assumes PLR based on statistically extracted ranges from the literature. However, the accuracy and uncertainty of PLR depend on several parameters including seasonality, local climatic conditions, and the response of a particular PV technology. In addition, the specific data pipeline and statistical method used affect the accuracy and uncertainty. To provide insights, a framework of (≈200 million) synthetic simulations of PV performance datasets using data from different climates is developed. Time series with known PLR and data quality are synthesized, and large parametric studies are conducted to examine the accuracy and uncertainty of different statistical approaches over the contiguous US, with an emphasis on the publicly available and “standardized” library, RdTools. In the results, it is confirmed that PLRs from RdTools are unbiased on average, but the accuracy and uncertainty of individual PLR estimates vary with climate zone, data quality, PV technology, and choice of analysis workflow. Best practices and improvement recommendations based on the findings of this study are provided.

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Section: Rdtools Potential Improvement Recommendationsmentioning

confidence: 99%

Section: Climate and Data Quality Sensitivity Of The Rdtools Yoy Appr...mentioning

confidence: 99%