Ångström–Prescott equation: Physical basis, empirical models and sensitivity analysis

Paulescu, Marius; Stefu, Nicoleta; Calinoiu, Delia; Paulescu, Eugenia; Pop, Nicolina; Boata, Remus; Mares, Oana

doi:10.1016/j.rser.2016.04.012

Cited by 84 publications

(50 citation statements)

References 43 publications

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“…For example, Model 6 in Zone 1, Model 3 in Zones 2 and 3, Model 8 in Zones 4 and 5, and Model 5 in Zone 6 performed best, with all these models taking altitude into account, which implies that altitude was the main factor determining the A‐P parameters in most regions. Our results concurred with those of Paulescu et al (), which demonstrated that altitude is a mandatory input variable to the A‐P model. De Souza et al () also revealed that a is dependent on altitude, while b is dependent on latitude and altitude.…”

Section: Discussionsupporting

confidence: 93%

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Determining the Parameters of the Ångström‐Prescott Model for Estimating Solar Radiation in Different Regions of China: Calibration and Modeling

Liu

Tan

Pan

2019

Earth and Space Science

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The Ångström‐Prescott model (referred to as the A‐P model) is one of the most accurate and widely used models for estimating global solar radiation (Rs). In the absence of Rs measurements and given the regional discrepancy of model parameters, it is crucial to increase the availability of these parameters and the applicability of parameter‐predicted models in different regions. In this study, we evaluated and compared the applicability and performance of the calibrated model and eight predictive models in terms of A‐P model parameters, using daily Rs and meteorological data from 105 radiation stations in seven natural geographic zones in China. These models were evaluated based on their coefficient of determination (R2), root mean square error, Nash‐Sutcliffe efficiency coefficients, percent bias, and global performance indexes. Results indicated that altitude was the main factor determining the Ångström‐Prescott parameters in most regions. All models performed well, with acceptable accuracy across the whole country; however, their performances varied among regions. The best performing predictive models for the northeast region (Zone 1), north China (Zone 2), central China (Zone 3), south China (Zone 4), Inner Mongolia (Zone 5), northwest region (Zone 6), and Qinghai‐Tibet region (Zone 7) were obtained: These were Models 6, 1, 7, 3, 6, 1, and 7, respectively. The present results support the application of these predictive models for the estimation of daily global Rs in the corresponding regions of China, where measured Rs data are not available, and possibly in other regions with a similar climate.

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Section: Discussionsupporting

confidence: 93%

“…Parameter b reflects the transport properties (aerosol density) of a cloudless atmosphere, affected by altitude and depends mainly on the total water content and turbidity of the atmosphere (Liu et al, ). The sum ( a + b ) represents the clearness index under a clear sky and increases slightly with altitude (Paulescu et al, ).…”

Section: Discussionmentioning

confidence: 99%

Determining the Parameters of the Ångström‐Prescott Model for Estimating Solar Radiation in Different Regions of China: Calibration and Modeling

Liu

Tan

Pan

2019

Earth and Space Science

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“…Similarly, the monthly average hourly clearness index ( k t ) values are calculated and reported in Table 8, which is the ratio of the global solar irradiance on a horizontal surface (I) to the hourly extraterrestrial solar irradiance on a horizontal surface (I o ), as given in Eq. (60).…”

Section: Resultsmentioning

confidence: 99%

“…Thus, the Angstrom-Prescott correlation, which represents the simple, linear, and pioneering relationship between clearness index and relative sunshine, was established by Angstrom and then was modified by Prescott [58,59]. Over the last decades, there were considerable endeavors for evaluating and interpreting the Angstrom-Prescott equation [60]. New formulations (either linear or non-linear) of the Angstrom-Prescott equation were proposed by many researchers using clearness index against sunshine fraction [8,36,47,57,[61][62][63][64][65][66][67][68][69], ambient temperature [8,47,62,67,69,70], relative humidity [8,47,69], precipitation [47,62,71,72], cloud cover [47,57,61,73], and multi-parameters [47,60,67,69,74].…”

Section: Existing Models and Methodsmentioning

confidence: 99%

Models and hierarchical methodologies for evaluating solar energy availability under different sky conditions toward enhancing concentrating solar collectors use: Texas as a case study

Al-Aboosi

2019

Int J Energy Environ Eng

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The precise estimation of solar radiation data is substantial in the long-term evaluation for the techno-economic performance of solar energy conversion systems (e.g., concentrated solar thermal collectors and photovoltaic plants) for each site around the world, particularly, direct normal irradiance which is utilized commonly in designing solar concentrated collectors. However, the lack of direct normal irradiance data comparing to global and diffuse horizontal irradiance data and the high cost of measurement equipment represent significant challenges for exploiting and managing solar energy. Consequently, this study was performed to develop two hierarchical methodologies by using various models, empirical correlations and regression equations to estimate hourly solar irradiance data for various worldwide locations (using new correlation coefficients) and different sky conditions (using cloud cover range). Additionally, the preliminary assessment for the potential of solar energy in the selected region was carried out by developing a comprehensive analysis for the solar irradiance data and the clearness index to make a proper decision for the capability of utilizing solar energy technologies. A case study for the San Antonio region in Texas was selected to demonstrate the accuracy of the proposed methodologies for estimating hourly direct normal irradiance and monthly average hourly direct normal irradiance data at this region. The estimated data show a good accuracy comparing with measured solar data by using locally adjusted coefficients and different statistical indicators. Furthermore, the obtained results show that the selected region is unequivocally amenable to harnessing solar energy as the prime source of energy by utilizing concentrating and non-concentrating solar energy systems.

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“…Ångström-Prescott (AP) is a model used to estimate the global solar irradiation as a function of solar radiation at the earth's surface and daily sunshine duration [15]. Akarslan et al [16] presents five semi-empirical solar radiation forecasting models based on the AP approach.…”

Section: Semi-empirical Methodsmentioning

confidence: 99%

Pattern Recognition and Its Application in Solar Radiation Forecasting

Ghofrani¹,

Azimi²,

Youshi³

2019

Pattern Recognition - Selected Methods and Applications

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As intermittent renewable energy sources such as wind and solar proliferate, the power systems operation and planning become more complicated due to increased uncertainties and variabilities. Accurate forecasting of these sources facilitates planning and operating the electric grid to integrate wind/solar power more reliably and efficiently. The neural network learning process can be disrupted by anomalies of wind/solar time-series data, which results in less accurate forecasting. By processing and analyzing wind/solar time-series data, machine learning and pattern recognition methods such as data clustering and classification can significantly enhance the forecast accuracy. This chapter reviews the various machine learning and pattern recognition methods proposed in the literature for time-series forecasting of solar radiation.

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Ångström–Prescott equation: Physical basis, empirical models and sensitivity analysis

Cited by 84 publications

References 43 publications

Determining the Parameters of the Ångström‐Prescott Model for Estimating Solar Radiation in Different Regions of China: Calibration and Modeling

Determining the Parameters of the Ångström‐Prescott Model for Estimating Solar Radiation in Different Regions of China: Calibration and Modeling

Models and hierarchical methodologies for evaluating solar energy availability under different sky conditions toward enhancing concentrating solar collectors use: Texas as a case study

Pattern Recognition and Its Application in Solar Radiation Forecasting

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