Empirical models for the estimation of global solar radiation with sunshine hours on horizontal surface for Jharkhand (India)

2016 ◽  
Vol 52 (3) ◽  
pp. 164-172 ◽  
Author(s):  
K. Namrata ◽  
S. P. Sharma ◽  
S. B. L. Seksena
2020 ◽  
Vol 12 (1) ◽  
pp. 32-39
Author(s):  
R.S. Sa’id ◽  
S.I. Akor ◽  
U.M. Gana

This paper proposes empirical correlation models for estimating global solar radiation using data of sunshine hours for the location of Makurdi in Benue State of Nigeria. The paper suggests extrapolation of the empirical models for other locations with similar climatic conditions. The proposed models are: Linear, Quadratic, Cubic, Exponential, Power and Logarithmic models. Each of the models is based on Angstrom-Prescott equations for estimating global solar radiation. Any of the models can ease the use of sophisticated equipments, which are expensive, delicate and sometimes develop faults during measurement. The results of the models show that the cubic model is the best with slightly higher coefficient of  determination. The coefficient of  determination of each of the models was found to be 0.952, 0.965, 0.967, 0.965, 0.948& 0.924 respectively, while the absolute correlation was found to be unity. Errors evaluated include MBE, RMSE and MPE with minimal values. The percentage diffuse and direct solar radiations, clearness index and the diffuse fraction were also estimated using the models. The results of the estimations done using the proposed models indicate that there is an estimated average annual global solar radiation of 6056MJm-2, monthly value of 505MJm-2 and daily insolation of 16.82MJm-2 sufficient enough for maximum solar radiation exploitation. Keywords: Solar Radiation, Empirical Models, Diffuse Radiation, Direct Radiation


BIBECHANA ◽  
2014 ◽  
Vol 11 ◽  
pp. 25-33
Author(s):  
Krishna R Adhikari ◽  
Shekhar Gurung ◽  
Binod K Bhattarai

Solar radiation is the best option and cost effective energy resources of this globe. Only a few stations are there in developing and under developed countries including Nepal to monitor solar radiation and sunshine hours to generate a rational and accurate solar energy database. In this study, daily global solar radiation, and ubiquitous meteorological data (temperature and relative humidity) rather than rarely available sunshine hours have been used for Biratnagar, Kathmandu, Pokhara and Jumla to derive regression constants and hence to develop an empirical model. The model estimated global solar radiation is found to be in close agreement with measured values of respective sites. The estimated values were compared with Angstrom-Prescott model and examined using the statistical tools. Thus, the linear regression technique can be used to develop model at any location in the world. The resultant model may then be used to estimate the missing data of solar radiation for the respective sites and also can be used to estimate global solar radiation for the locations of similar geographic and meteorological characteristic. DOI: http://dx.doi.org/10.3126/bibechana.v11i0.10376   BIBECHANA 11(1) (2014) 25-33


2019 ◽  
Vol 44 (2) ◽  
pp. 168-188
Author(s):  
Shaban G Gouda ◽  
Zakia Hussein ◽  
Shuai Luo ◽  
Qiaoxia Yuan

Utilizing solar energy requires accurate information about global solar radiation (GSR), which is critical for designers and manufacturers of solar energy systems and equipment. This study aims to examine the literature gaps by evaluating recent predictive models and categorizing them into various groups depending on the input parameters, and comprehensively collect the methods for classifying China into solar zones. The selected groups of models include those that use sunshine duration, temperature, dew-point temperature, precipitation, fog, cloud cover, day of the year, and different meteorological parameters (complex models). 220 empirical models are analyzed for estimating the GSR on a horizontal surface in China. Additionally, the most accurate models from the literature are summarized for 115 locations in China and are distributed into the above categories with the corresponding solar zone; the ideal models from each category and each solar zone are identified. Comments on two important temperature-based models that are presented in this work can help the researchers and readers to be unconfused when reading the literature of these models and cite them in a correct method in future studies. Machine learning techniques exhibit performance GSR estimation better than empirical models; however, the computational cost and complexity should be considered at choosing and applying these techniques. The models and model categories in this study, according to the key input parameters at the corresponding location and solar zone, are helpful to researchers as well as to designers and engineers of solar energy systems and equipment.


Author(s):  
Abdul Basit Da’ie

Solar energy properties such as Global Solar Radiation (GSR) intensity could be determined in either methods, experimentally or theoretically. Unfortunately, in most countries including Afghanistan, the first method which is more acceptable, but due to the high cost, maintenance and calibration requirements is not available. Therefore, an alternative widely used way is the second one which is model developments based on the meteorological (atmospheric) data; specially the sunny hours. The aim of this study at Shakardara area is to estimate atmospheric transparency percentage on 2017, determining the angstrom model coefficients and to introduce a suitable model for global solar radiation prediction. The hourly observed solar radiation intensity H (WHm-2 ) and sunshine hours S (


2014 ◽  
Vol 5 (1) ◽  
pp. 669-680
Author(s):  
Susan G. Lakkis ◽  
Mario Lavorato ◽  
Pablo O. Canziani

Six existing models and one proposed approach for estimating global solar radiation were tested in Buenos Aires using commonly measured meteorological data as temperature and sunshine hours covering the years 2010-2013. Statistical predictors as mean bias error, root mean square, mean percentage error, slope and regression coefficients were used as validation criteria. The variability explained (R2), slope and MPE indicated that the higher precision could be excepted when sunshine hours are used as predictor. The new proposed approach explained almost 99% of the RG variability with deviation of less than ± 0.1 MJm-2day-1 and with the MPE smallest value below 1 %. The well known Ångström-Prescott methods, first and third order, was also found to perform for the measured data with high accuracy (R2=0.97-0.99) but with slightly higher MBE values (0.17-0.18 MJm-2day-1). The results pointed out that the third order Ångström type correlation did not improve the estimation accuracy of solar radiation given the highest range of deviation and mean percentage error obtained.  Where the sunshine hours were not available, the formulae including temperature data might be considered as an alternative although the methods displayed larger deviation and tended to overestimate the solar radiation behavior.


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