The diffuse-global correlation: Its application to estimating solar radiation on tilted surfaces in Marrakesh, Morocco

Zeroual, Abdelouhab; Ankrim, M.; Wilkinson, A.J.

doi:10.1016/0960-1481(95)00116-6

Cited by 22 publications

(6 citation statements)

References 18 publications

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“…The validation involved using statistical indicators, namely, the coefficient of determination (R 2 ), the root mean square error (RMSE), the mean bias error (MBE), and the mean percentage error (MPE) (Malik and Tamam 1995;Omer 1994;Tiris et al 1996;Zeroual et al 1996). R 2 measures the degree of success of a fit in explaining data variation.…”

Section: Statistical Methods For Performance Assessmentmentioning

confidence: 99%

Estimating daily global solar radiation during the growing season in Northeast China using the Ångström–Prescott model

Zhao

et al. 2011

Theor Appl Climatol

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Daily global solar radiation is an important input required in most crop models. In the present study, a sunshine-based model, the Ångström-Prescott model, is employed to estimate daily global solar radiation on a horizontal surface during the growing season in Northeast China. Data from six control groups are used. The controls include the entire sequence, precipitation days, and non-precipitation days both during the growing season and year-round. Estimations are validated by comparing the calculated values with the corresponding measured values. The results indicate that estimating daily global solar radiation during the growing season using data only from the growing season is better than using year-round data. Classifying days with respect to precipitation and non-precipitation is also unnecessary. The performance on estimating daily global solar radiation during the growing season using the entire data in growing season performs best. A sunshine-based equation is obtained using our method to estimate growing season daily radiation for all meteorological stations in Northeast China. The approved approach is expected to be beneficial to crop models and other agricultural purposes.

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Section: Statistical Methods For Performance Assessmentmentioning

confidence: 99%

Estimating daily global solar radiation during the growing season in Northeast China using the Ångström–Prescott model

Zhao

et al. 2011

Theor Appl Climatol

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“…There are numerous statistical methods available in literatures dealing the assessment and comparison of solar radiation data . The three statistical indicators, viz.…”

Section: Methodsmentioning

confidence: 99%

A Comprehensive Study of Ground Measurement and Satellite‐Derived Data of Global and Diffuse Radiation

Mahima

Karakoti

Pathak

et al. 2018

Env Prog and Sustain Energy

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The comparative study of measured and calculated data of India Meteorological Department (IMD) and National Aeronautics and Space Administration (NASA) for 23 locations of India for 15 years (1986–2000) is presented. Global and diffuse radiation data are used in the study and to make the study comprehensive, three statistical predictors, viz., mean percentage error (MPE), root mean square error (RMSE), and mean bias error (MBE) are used for the comparison. The results show that for global radiation, MPE is maximum of −36.65% for Shillong and minimum of 0.93% for New Delhi. MBE is found to be maximum of 1.63 kWh/m2 for Shillong while it is minimum (0.04 kWh/m2) for Nagpur. RMSE is maximum (2.14 kWh/m2) for Shillong and minimum for Ranchi (0.19 kWh/m2). For diffuse radiation, MPE is highest for Bhavnagar (100%) and lowest for Hyderabad (81.33%). MBE is maximum of −7.73 kWh/m2 and minimum of −4.62 kWh/m2 for Srinagar. RMSE is maximum for Bhavnagar (8.26 kWh/m2) and minimum for Srinagar (4.73 kWh/m2). © 2018 American Institute of Chemical Engineers Environ Prog, 38:e13060, 2019

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“…There are numerous statistical methods available in solar energy literature, which deal with the assessment and comparison of solar radiation estimation models [18][19][20][21][22][23][24][25][26][27]. In the present study statistical indicators, namely root mean square error (RMSE) and mean bias error (MBE) have been used.…”

Section: Statistical Methods Usedmentioning

confidence: 99%

Evaluation and comparison of hourly solar radiation models

Ahmad

Tiwari

2009

Int. J. Energy Res.

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In this paper, an attempt has been made to develop a new model to evaluate the hourly solar radiation for composite climate of New Delhi. The comparison of new model for hourly solar radiation has been carried out by using various model proposed by others. The root mean square error (RMSE) and mean bias error (MBE) have been used to compare the accuracy of new and others model. The results show that the ASHRAE and new proposed model estimate hourly solar radiation better for composite climate of New Delhi in comparison to other models. Hourly solar radiation estimated by constants obtained by new model (modified ASHRAE model) for composite climate of India is fairly comparable with measured data. The percentage mean bias error with new constants for New Delhi was found as low as 0.15 and 0% for hourly beam and diffuse radiation, respectively. There is a 1.9-8.5% RMSE between observed and predicted values of beam radiation using new constants for clear days. The statistical analysis has been used for the present study.

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The diffuse-global correlation: Its application to estimating solar radiation on tilted surfaces in Marrakesh, Morocco

Cited by 22 publications

References 18 publications

Estimating daily global solar radiation during the growing season in Northeast China using the Ångström–Prescott model

Estimating daily global solar radiation during the growing season in Northeast China using the Ångström–Prescott model

A Comprehensive Study of Ground Measurement and Satellite‐Derived Data of Global and Diffuse Radiation

Evaluation and comparison of hourly solar radiation models

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