Quantification of the aerosol-induced errors in solar irradiance modeling

Blaga, Robert; Calinoiu, Delia; Stefu, Nicoleta; Boata, Remus; Sabadus, Andreea; Paulescu, Eugenia; Pop, Nicolina; Mares, Oana; Bojin, Sorin; Paulescu, Marius

doi:10.1007/s00703-021-00815-z

Cited by 7 publications

(9 citation statements)

References 35 publications

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“…Very few studies focus on the aerosol impact on the separation of clear-sky global solar irradiance into its primary components direct-normal and diffuse. In a recent study, the classical diffuse fraction was explored as an appropriate quantifier for the fractional part of the global solar irradiance estimated by a clear-sky solar irradiance model as being diffuse [14]. This work continues the study [14], revealing new capabilities of the clear-sky diffuse fraction as a quantifier of the aerosol impact on the global solar irradiance.…”

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confidence: 72%

“…In Ref. [14] the estimation errors are separated into two classes: (A) intrinsic/general model errors and (B) aerosolrelated errors. The aerosol-related errors may be further divided into two sub-classes: (B1) the inability of the solar irradiance model in capturing the aerosol influence on the atmospheric transmittance and (B2) the uncertainty in the input parameters associated with aerosols.…”

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confidence: 99%

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Short-Term PV Power Forecasting Based on Sky Imagery. A Case Study at the West University of Timisoara

Dughir

Sabadus

Stefu

et al. 2022

Annals of West University of Timisoara - Physics

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This study deals with the performance of PV2-state model in intra-hour forecasting of photovoltaic (PV) power. The PV2-state model links an empirical model for estimating the PV power delivered by a PV system under clear-sky with a model for forecasting the relative position of the Sun and clouds. Sunshine number (SSN), a binary quantifier showing if the Sun shines or not, is used as a measure for the Sun position with respect to clouds. A physics-based approach to intra-hour forecasting, processing cloud field information from an all-sky imager, is applied to predict SSN. The quality of SSN prediction conditions the overall quality of PV2-state forecasts. The PV2-state performance was evaluated against a challenging database (high variability in the state-of-the-sky, thin cloud cover, broken cloud field, isolated passing clouds) comprising radiometric data and sky-images collected on the Solar Platform of the West University of Timisoara, Romania. The investigation was performed from two perspectives: general model accuracy and, as a novelty, identification of characteristic elements in the state-of-the-sky which fault the SSN prediction. The outcome of such analysis represents the basis of further research aiming to increase the performance in PV power forecasting.

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confidence: 72%

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confidence: 99%

Short-Term PV Power Forecasting Based on Sky Imagery. A Case Study at the West University of Timisoara

Dughir

Sabadus

Stefu

et al. 2022

Annals of West University of Timisoara - Physics

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“…M1 is the result of intensive research on the effect of various predictors on the diffuse fraction. It gathers together almost all the predictors successfully accounted for by the previous separation models: (1) the clearness index k t , (2) the deviation of the clearness index from its estimated value under clear skies ∆k tc , (defined by Equation (A3)), (3) the part of diffuse fraction k d that is attributable to cloud enhancement k de , (defined by Equation (A4)), (4) daily average of the clearness index k day , (defined by Equation (A1)), (5) hourly average of the clearness index k hour , (6) the persistence factor ψ, defined as the average of a lag and a lead of the clearness index values, (7) the clear sky global solar irradiance G cs in MJ/(h•m 2 ), and (8) the ratio of measured GHI and the estimated global clear sky irradiance K csi . M1 is defined by the empirical equation (R 2 = 0.929 and nMBE = 0.011):…”

Section: A Proposal For Minute-scale Ghi Separation Modelsmentioning

confidence: 99%

“…Along with this basic application, the evaluation of DHI and DNI on the basis of GHI, which is currently the diffuse fraction, acquires new values, such as its use as a quantifier of the uncertainty induced by aerosols in estimating clear-sky DHI [7] or as a proxy for classifying the sky conditions into clear, intermediate, and overcast [8].…”

Section: Introductionmentioning

confidence: 99%

Minute-Scale Models for the Diffuse Fraction of Global Solar Radiation Balanced between Accuracy and Accessibility

Paulescu

2023

Applied Sciences

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The separation models are tools used in solar engineering to estimate direct normal (DNI) and diffuse horizontal (DHI) solar irradiances from measurements of global solar irradiance (GHI). This paper proposes two empirical separation models that stand out owing to their simple mathematical formulation: a rational polynomial equation. Validation of the new models was carried out against data from 36 locations, covering the four major climatic zones. Five current top minute-scale separation models were considered references. The tests were performed on the final products of the estimation: DNI and DHI. The first model (M1) operates with eight predictors (evaluated from GHI post-processed measurements and clear-sky counterpart estimates) and constantly outperforms the already established models. The second model (M2) operates with three predictors based only on GHI measurements, which gives it a high degree of accessibility. Based on a statistical linear ranking method according to the models’ performance at every station, M1 leads the hierarchy, ranking first in both DNI and DHI estimation. The high accessibility of the M2 does not compromise accuracy; it is proving to be a real competitor in the race with the best-performing current models.

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“…The REST2 model is universally recognized as one of the most accurate models available in the literature [13], and a model which maintains a good performance under all atmospheric conditions [14]. The v5 version of REST2 is used here as a reference model 100 [15].…”

Section: Clear-sky Modelsmentioning

confidence: 99%

Bias-Correction of Diffuse Solar Irradiance Modeled Through Parametric Models

Blaga

2022

Annals of West University of Timisoara - Physics

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In this paper, a bias-correction of clear-sky models in prediction of the diffuse solar irradiance is presented. High quality ground date from two networks is used for model input and validation: aerosol optical depth from AERONET and diffuse solar irradiance from BSRN. Four established clear-sky models are tested: REST2, Yang, PS and MAC2. Two types of bias-corrections are developed: i) a correction for multiple aerosol scattering (MSC), accounting for extinction at high turbidity, and ii) a general regression on the model errors as a function of Sun elevation angle (h) and turbidity (β) correction (MOS). While the impact of MSC proves marginal, the MOS correction shows good results. Two versions of MOS are developed, one on the global dataset and a site-specific adaptation. Both versions bring significant improvements over the original implementation of the models, while the site-specific version reduces the errors with a few additional percentage points. The aggregate nRMSE of the REST2 model is reduced from 17.8% (no correction) to 11.3% (global MOS) and 9.4% (site-specific MOS).

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Quantification of the aerosol-induced errors in solar irradiance modeling

Cited by 7 publications

References 35 publications

Short-Term PV Power Forecasting Based on Sky Imagery. A Case Study at the West University of Timisoara

Short-Term PV Power Forecasting Based on Sky Imagery. A Case Study at the West University of Timisoara

Minute-Scale Models for the Diffuse Fraction of Global Solar Radiation Balanced between Accuracy and Accessibility

Bias-Correction of Diffuse Solar Irradiance Modeled Through Parametric Models

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