Uncertainty in monthly GHI due to daily data gaps

Peruchena, Carlos F.; Amores, Antonio F.

doi:10.1016/j.solener.2017.08.080

Cited by 7 publications

(1 citation statement)

References 7 publications

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“…The sum of the direct and diffuse components which reaches the Earth's surface both on a horizontal (global horizontal solar irradiance, GHI) and tilted (global tilted solar irradiance, GTI) planes is of major interest for non-concentrating photovoltaics (PV). Due to the significant short-term and long-term variability in solar irradiance [1][2][3], the solar resource assessment should consider solar irradiance time series-and not just mean annual averages-to understand the solar dynamics over intraday, daily and seasonal scales [4][5][6] and the interannual variability which defines the plant performance in different probability of exceedance scenarios [7][8][9][10]. These scenarios, as well as their corresponding uncertainties, are modeled to evaluate a project's ability to return the investment for these projects [10,11].…”

Section: Introductionmentioning

confidence: 99%

Site-Adaptation of Modeled Solar Radiation Data: The SiteAdapt Procedure

et al. 2020

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The adaptation of modeled solar radiation data with coincident ground measurements has become a standard practice of the industry, typically requested by financial institutions in the detailed solar resource assessments of solar projects. This practice mitigates the risk of solar projects, enhancing the adequate solar plant design and reducing the uncertainty of its yield estimates. This work presents a procedure for improving the accuracy of modeled solar irradiance series through site-adaptation with coincident ground-based measurements relying on the use of a regression preprocessing followed by an empirical quantile mapping (eQM) correction. It was tested at nine sites in a wide range of latitudes and climates, resulting in significant improvements of statistical indicators of dispersion, distribution similarity and overall performance: relative bias is reduced on average from −1.8% and −2.3% to 0.1% and 0.3% for GHI and DNI, respectively; relative root mean square deviation is reduced on average from 17.9% and 34.9% to 14.6% and 29.8% for GHI and DNI, respectively; the distribution similarity is also improved after the site-adaptation (KSI is 3.5 and 3.9 times lower for GHI and DNI at hourly scale, respectively). The methodology is freely available as supplementary material and downloadable as R-package from SiteAdapt.

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Section: Introductionmentioning

confidence: 99%

Site-Adaptation of Modeled Solar Radiation Data: The SiteAdapt Procedure

et al. 2020

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Semi-Automatic Feature Extraction to Assess Solar Potential for Rural India

Pathak¹,

Kapoor²,

Garg³

2019

Preprint

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Quantitative analysis of high-frequency radiometric databases. Application to the case of Seville

Pérez-Aparicio,

Moreno-Tejera,

Silva-Pérez

et al. 2024

Theor Appl Climatol

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The short-term variability of solar resource is one of the main challenges faced by the large-scale implementation of photovoltaic (PV) systems today; this will increase as the installed power of solar PV increases. To evaluate its influence on electrical grid operation, it is essential to analyse the variability of solar resource based on high-frequency data, 30 s and shorter. In this study, the high-frequency measurements of Global Horizontal Irradiation (GHI) and Direct Normal Irradiation (DNI) recorded every 5 s at the radiometric station of the University of Seville for 20 years are analysed and characterised. For this purpose, a corrected database for GHI and DNI was obtained from the application of a correction methodology. To understand the reliability of this database it is required to know its uncertainty. This work proposes a methodology to quantify the uncertainty of high-frequency radiometric databases whose wrong data have been corrected. Applying it, the daily uncertainty is 2.69% for GHI and 4.07% for DNI. To characterize the distribution of the database, the distribution of high frequency kt and kb indices are modelled providing the fitting parameters. To characterize the variability of high-frequency measurements, transition matrices are proposed, which allow identifying both the magnitude and frequency of jumps. The results obtained show that jumps of up to 800 W/m2 occur in both GHI and DNI. In the case of GHI, the percentage of jumps equal to or greater than 500 W/m2 is 1.36%, about 43,000 jumps in a year in measurements every 5 s.

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Uncertainty in monthly GHI due to daily data gaps

Cited by 7 publications

References 7 publications

Site-Adaptation of Modeled Solar Radiation Data: The SiteAdapt Procedure

Site-Adaptation of Modeled Solar Radiation Data: The SiteAdapt Procedure

Semi-Automatic Feature Extraction to Assess Solar Potential for Rural India

Quantitative analysis of high-frequency radiometric databases. Application to the case of Seville

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