Article

# Correlation between global solar radiation and air temperature in Asturias, Spain

Department of Physics, University of Oviedo, Ave. Calvo Sotelo s/n E-33007 Oviedo, Spain

Solar Energy (Impact Factor: 3.54). 07/2009; 83(7):1076-1085. DOI: 10.1016/j.solener.2009.01.012 - [Show abstract] [Hide abstract]

**ABSTRACT:**This paper presents a new methodology to build parametric models to estimate global solar irradiation adjusted to specific on-site characteristics based on the evaluation of variable importance. Thus, those variables higly correlated to solar irradiation on a site are implemented in the model and therefore, different models might be proposed under different climates. This methodology is applied in a study case in La Rioja region (northern Spain). A new model is proposed and evaluated on stability and accuracy against a review of twenty-two already existing parametric models based on temperatures and rainfall in seventeen meteorological stations in La Rioja. The methodology of model evaluation is based on bootstrapping, which leads to achieve a high level of confidence in model calibration and validation from short time series (in this case five years, from 2007 to 2011). The model proposed improves the estimates of the other twenty-two models with average mean absolute error (MAE) of 2.195 MJ/m2day and average confidence interval width (95% C.I., n=100) of 0.261 MJ/m2day. 41.65% of the daily residuals in the case of SIAR and 20.12% in that of SOS Rioja fall within the uncertainty tolerance of the pyranometers of the two networks (10% and 5%, respectively). Relative differences between measured and estimated irradiation on an annual cumulative basis are below 4.82%. Thus, the proposed model might be useful to estimate annual sums of global solar irradiation, reaching insignificant differences between measurements from pyranometers.Renewable Energy 06/2013; 60:604-614. · 3.36 Impact Factor - [Show abstract] [Hide abstract]

**ABSTRACT:**Estimation of solar radiation is considered as the most important parameter for the design and development of various solar energy systems. But, the availability of the required data is very scarce and often not readily accessible. The foremost objective of the present study was to evaluate the various models for the estimation of monthly average global solar radiation from bright sunshine hours and other meteorological parameters at four locations namely Sri Aman, Sibu, Bintulu and Limbang in the Sarawak State. For this purpose, six different solar radiation models, such as Angstrom, Glover, Tasdemirglu, Bahel, Hargreaves and Sayigh, have been investigated. The computed values are compared and assessed, but no relationships of results were found among selected models and with measured values of the nearest location. Hence, a simple and more flexible model is introduced based on the input data of bright sunshine hours, relative humidity and maximum temperature, for the prediction of available global solar radiation on a horizontal surface. The required data for the suggested model is usually available in the most meteorological sites. The proposed model demonstrated acceptable results, and statistically displayed lower RMSE and MBE as compared to the examined models. It could be a good estimator for predicting the global solar radiation in coastal and humid areas.01/2010; - [Show abstract] [Hide abstract]

**ABSTRACT:**In this study, a multilayer feed forward (MLFF) neural network based on back propagation algorithm was developed, trained, and tested to predict monthly mean daily global radiation in Tamil Nadu, India. Various geographical, solar and meteorological parameters of three different locations with diverse climatic conditions were used as input parameters. Out of 565 available data, 530 were used for training and the rest were used for testing the artificial neural network (ANN). A 3-layer and a 4-layer MLFF networks were developed and the performance of the developed models was evaluated based on mean bias error, mean absolute percentage error, root mean squared error and Student’s t-test. The 3-layer MLFF network developed in this study did not give uniform results for the three chosen locations. Hence, a 4-layer MLFF network was developed and the average value of the mean absolute percentage error was found to be 5.47%. Values of global radiation obtained using the model were in excellent agreement with measured values. Results of this study show that the designed ANN model can be used to estimate monthly mean daily global radiation of any place in Tamil Nadu where measured global radiation data are not available.Journal of Earth System Science 121(6). · 0.70 Impact Factor

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