Generation of Typical Solar Radiation Year for Mediterranean Region of Turkey

Bulut, Hüsamettin; Büyükalaca, Orhan; Yılmaz, Alper

doi:10.1080/15435070902784970

Cited by 12 publications

(7 citation statements)

References 11 publications

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“…Since solar radiation data can vary from year to year, there is a need to generate a customized solar radiation database that can well represent the long-term averaged solar radiation over a year [31,[34][35][36][37]. In this subsection, the Typical Meteorological Year (TMY) method is recommended and applied.…”

Section: Methods and Process For Forming Monthly Mean Daily Global Solmentioning

confidence: 99%

Determination of the Optimal Tilt Angle of Solar Collectors for Different Climates of China

et al. 2016

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Abstract:The tilt angle with the horizon (with respect to the ground) of the solar energy system affects the amount of solar radiation received. This paper suggests a simple and universal method to obtain the optimum tilt angles by estimating the monthly mean daily global solar radiation on tilted surfaces facing directly towards the equator, which is based on monthly average daily global solar radiation data produced from Typical Meteorological Year (TMY) data. The monthly, seasonal, and yearly optimum tilt angles for photovoltaic panels are calculated at six stations of different climatic types (Tropical Zone (TZ), Subtropical Zone (SZ), Warm Temperate Zone (WTZ), Mid Temperate Zone (MTZ), Cold Temperate Zone (CTZ) and Tibetan Plateau Zone (TPZ)). The results indicate that changing the monthly, seasonal, and yearly optimum tilt angles causes a significant yearly gain in the solar radiation for the region. In addition, general correlations are generated to estimate the optimum tilt angle of solar collectors at six typical climatic stations of China. The performances of the proposed models are compared using statistical error tests such as the mean absolute bias error (MABE), the root mean square error (RMSE) and the correlation coefficients (R).

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Section: Methods and Process For Forming Monthly Mean Daily Global Solmentioning

confidence: 99%

Determination of the Optimal Tilt Angle of Solar Collectors for Different Climates of China

et al. 2016

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“…For such regions empirical models have to be developed using climatological measured pyranometer data from available stations. These models are then used to estimate radiation values at other locations of interest (Mohandes, Rehman, and Halawani 1998;Bulut, Büyükalaca, and Yılmaz 2009;Kassem, Aboukarima, and El Ashmawy 2009).…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Networks for Predicting Global Solar Radiation in Al Ain City - Uae

Al-Shamisi

Assi

Hejase

2013

International Journal of Green Energy

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The geographical location (latitude: 24 • 16 N and longitude: 55 • 36 E) of Al Ain city in the southwest of United Arab Emirates (UAE) favors the development and utilization of solar energy. This paper presents an artificial neural network (ANN) approach for predicting monthly global solar radiation (MGSR) on a horizontal surface in Al Ain. The ANN models are presented and implemented on 13-year measured meteorological data for Al Ain such as maximum temperature, mean wind speed, sunshine, and mean relative humidity between 1995 and 2007. The meteorological data between 1995 and 2004 are used for training the ANN and data between 2004 and 2007 are used for testing the predicted values. Multilayer perceptron (MLP) and radial basis function (RBF) neural networks are used for the modeling. Models for the MGSR were obtained using eleven combinations of data sets based on the above mentioned measured data for Al Ain city. Forecasting performance parameters such as root mean square error (RMSE), mean bias error (MBE), mean absolute percentage error (MAPE), and correlation coefficient (R 2 ) are presented for the model. The values of RMSE, MBE, MAPE, and R 2 are found to be, respectively, 35%, 0.307%, 3.88%, and 92%. A comparison of estimated MGSR with regression models is carried out. The ANN model predicts better than other models. The estimated MGSR data are in reasonable agreement with the actual values. The results indicate the capability of the ANN technique over unseen data and its ability to produce accurate prediction models.

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“…Net radiation (R n ) flux drives global and local biogeochemical cycles, and its components are required as key input variables in modeling and simulation of environmental processes as well as in design of renewable energy applications (Bulut, Büyükalaca, and Yılmaz 2009;Abbas et al 2011;Escobedo et al 2011;Jain, Mehta, and Mittal 2011;Khatib et al 2011). The surface net radiation budget can be estimated as the sum of both net shortwave (R net s ) and net longwave (R net l ) radiation flux densities, generally expressed in units of W m −2 , at the Earth's surface thus:…”

Section: Introductionmentioning

confidence: 99%

Dynamic Emulations of Surface Radiation Components During Day and Night Under all Sky and Surface Conditions Using Temporal Neural Networks

Evrendilek

Celik

2013

International Journal of Green Energy

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Modeling of land surface radiation budget and its components is essential to a better understanding of soil-vegetation-atmosphere interactions. Time Delay Neural Network (TDNN)and Time Lag Recurrent Network (TLRN) models were used to emulate all the hourly surface radiation components for a temperate peatland during day and night under all-sky and -surface conditions. Sensitivity analyses of full versus reduced models, daytime versus nighttime periods, and TDNN versus TLRN models were carried out using training-, crossvalidation, and testing-derived metrics of root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R 2 ) for each of the components. The full daytime temporal neural network models performed best based on RMSE of 2.3 W m −2 in downwelling longwave radiation to 112.2 W m −2 in upwelling shortwave radiation; R 2 of 0.52 in downwelling longwave radiation to 0.88 in net shortwave radiation and net radiation; and MAE of 1.73 W m −2 in downwelling longwave radiation to 90.57 W m −2 in upwelling shortwave radiation. The best nighttime TDNN models led to RMSE values that ranged from 4.4 W m −2 in downwelling longwave radiation to 9.3 W m −2 in upwelling longwave radiation; R 2 values that ranged from 0.38 in net longwave radiation to 0.60 in downwelling longwave radiation; and MAE values that ranged from 4.1 W m −2 in downwelling longwave radiation to 8.1 W m −2 in upwelling longwave radiation. Temporal neural networks used in this study appear to be a promising approach to predict nonlinear behaviors of the daytime and nighttime surface radiation components.

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Generation of Typical Solar Radiation Year for Mediterranean Region of Turkey

Cited by 12 publications

References 11 publications

Determination of the Optimal Tilt Angle of Solar Collectors for Different Climates of China

Determination of the Optimal Tilt Angle of Solar Collectors for Different Climates of China

Artificial Neural Networks for Predicting Global Solar Radiation in Al Ain City - Uae

Dynamic Emulations of Surface Radiation Components During Day and Night Under all Sky and Surface Conditions Using Temporal Neural Networks

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