Evaluation of errors made in solar irradiance estimation due to averaging the Angstrom turbidity coefficient

Calinoiu, Delia; Stefu, Nicoleta; Paulescu, Marius; Trif-Tordai, G.; Mares, Oana; Paulescu, Eugenia; Boata, Remus; Pop, Nicolina; Pacurar, Angel

doi:10.1016/j.atmosres.2014.07.015

Cited by 12 publications

(3 citation statements)

References 28 publications

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“…The fluctuations are driven by cloud dynamics, atmospheric losses (Calinoiu et al, 2014), and the transport of airborne pollutants (Vindel and Polo, 2014a). Changes in irradiance that occur on the same time scale as changes in electricity demand will impact the benefits of storage and self-consumption in a domestic or community PV system (Marcos et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Stochastic generation of synthetic minutely irradiance time series derived from mean hourly weather observation data

et al. 2015

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Synthetic minutely irradiance time series are utilised in non-spatial solar energy system research simulations. It is necessary that they accurately capture irradiance fluctuations and variability inherent in the solar resource. This article describes a methodology to generate a synthetic minutely irradiance time series from widely available hourly weather observation data. The weather observation data are used to produce a set of Markov chains taking into account seasonal, diurnal, and pressure influences on transition probabilities of cloud cover. Cloud dynamics are based on a power-law probability distribution, from which cloud length and duration are derived. Atmospheric transmission losses are simulated with minutely variability, using atmospheric profiles from meteorological reanalysis data and cloud attenuation derived real-world observations. Both direct and diffuse irradiance are calculated, from which total irradiance is determined on an arbitrary plane. The method is applied to the city of Leeds, UK, and validated using independent hourly radiation measurements from the same site. Variability and ramp rate are validated using 1-min resolution irradiance data from the town of Cambourne, Cornwall, UK. The hourly irradiance frequency distribution correlates with R 2 ¼ 0:996 whilst the mean hourly irradiance correlates with R 2 ¼ 0:971, the daily variability indices cumulative probability distribution function (CDF), 1-min irradiance ramp rate CDF and 1-min irradiance frequency CDF are also shown to correlate with R 2 ¼ 0:9903; 1:000, and 0:9994 respectively. KolmogorovSmirnov tests on 1-min data for each day show that the ramp rate frequency of occurrence is captured with a high significance level of 99.99%, whilst the irradiance frequency distribution and minutely variability indices are captured at significances of 99% and 97.5% respectively. The use of multiple Markov chains and detailed consideration of the atmospheric losses are shown to be essential elements for the generation of realistic minutely irradiance time series over a typical meteorological year. A freely downloadable example of the model is made available and may be configured to the particular requirements of users or incorporated into other models.

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Section: Introductionmentioning

confidence: 99%

Stochastic generation of synthetic minutely irradiance time series derived from mean hourly weather observation data

et al. 2015

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“…To the authors' knowledge, no detailed investigation has systematically evaluated the direct impact on long-term modelled irradiance datasets of the AOD representation time scale. Nevertheless, some investigations have treated either the daily or monthly AOD time scales separately, while restricting the spatial scope to only local or regional scales (Gueymard, 2011;Calinoiu et al, 2014;Nikitidou et al, 2014). In the field of solar resource assessment, other studies have compared AOD datasets from different origins and with different time scales so that the relative importance of time scale was explicitly excluded from the discussion (Cebecauer and Šúri, 2010;Zubler et al, 2011).…”

Section: Introductionmentioning

confidence: 98%

Bias induced by the AOD representation time scale in long-term solar radiation calculations. Part 2: Impact on long-term solar irradiance predictions

Ruiz‐Arias

Gueymard²,

Santos-Alamillos

et al. 2016

Solar Energy

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“…The relative error in estimating global solar irradiance under clear-sky due to an inadequate consideration of the Ångström turbidity coefficient may be considerable. For instance, when the current value of the Ångström turbidity coefficient is replaced by the yearly average value, the relative errors in estimating global solar irradiance may be higher than 20% [12].…”

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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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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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Evaluation of errors made in solar irradiance estimation due to averaging the Angstrom turbidity coefficient

Cited by 12 publications

References 28 publications

Stochastic generation of synthetic minutely irradiance time series derived from mean hourly weather observation data

Stochastic generation of synthetic minutely irradiance time series derived from mean hourly weather observation data

Bias induced by the AOD representation time scale in long-term solar radiation calculations. Part 2: Impact on long-term solar irradiance predictions

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

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