Estimation of solar radiation using artificial neural networks with different input parameters for Mediterranean region of Anatolia in Turkey

被引:127
作者
Koca, Ahmet [2 ]
Oztop, Hakan F. [1 ,3 ]
Varol, Yasin [1 ]
Koca, Gonca Ozmen [4 ]
机构
[1] Firat Univ, Fac Technol, Dept Mech Engn, TR-23119 Elazig, Turkey
[2] Firat Univ, Tech Vocat Sch, TR-23119 Elazig, Turkey
[3] King Saud Univ, Coll Engn, Dept Mech Engn, Riyadh, Saudi Arabia
[4] Firat Univ, Dept Elect & Comp Educ, TR-23119 Elazig, Turkey
关键词
Solar radiation; Mediterranean region of Anatolia; Artificial neural network; PREDICTION; TEMPERATURE; MODELS; ELAZIG;
D O I
10.1016/j.eswa.2011.01.085
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
An artificial neural network (ANN) model was used to estimate the solar radiation parameters for seven cities from Mediterranean region of Anatolia in Turkey. As well known that Turkey is a bridge between Asia and Europe and it lies in a sunny belt, between 36 degrees and 42 degrees N latitudes. Indeed, the country has sufficient solar radiation intensities for solar applications. In order to make estimation of solar radiation, the data from the Turkish State and Meteorological Service were used. Data of 2006 were used for testing and data of 2005, 2007, and 2008 were estimated. Effects of number of input parameters were tested on solar radiation that was output layer. With this aim, number of input layer parameters changed from 2 to 6. The obtained results indicated that the method could be used by researchers or scientists to design high efficiency solar devices. It was also found that number of input parameters was the most effective parameter on estimation of future data on solar radiation. (C) 2011 Elsevier Ltd. All rights reserved.
引用
收藏
页码:8756 / 8762
页数:7
相关论文
共 35 条
[1]   Monthly average daily solar radiation and clearness index contour maps over Oman [J].
Al-Lawati, A ;
Dorvlo, ASS ;
Jervase, JA .
ENERGY CONVERSION AND MANAGEMENT, 2003, 44 (05) :691-705
[2]  
[Anonymous], RENEWABLE ENERGY
[3]  
[Anonymous], SOL ENERGY
[4]  
[Anonymous], 1994, Neural networks: a comprehensive foundation
[5]   Estimating the horizontal diffuse solar radiation over the Central Anatolia Region of Turkey [J].
Aras, Haydar ;
Balli, Ozgur ;
Hepbasli, Arif .
ENERGY CONVERSION AND MANAGEMENT, 2006, 47 (15-16) :2240-2249
[6]   Correlations for estimation of daily global solar radiation with hours of bright sunshine in Turkey [J].
Bakirci, Kadir .
ENERGY, 2009, 34 (04) :485-501
[7]   Application of artificial neural networks for the wind speed prediction of target station using reference stations data [J].
Bilgili, Mehmet ;
Sahin, Besir ;
Yasar, Abdulkadir .
RENEWABLE ENERGY, 2007, 32 (14) :2350-2360
[8]   Daily solar irradiation estimation over a mountainous area using artificial neural networks [J].
Bosch, J. L. ;
Lopez, G. ;
Batlles, F. J. .
RENEWABLE ENERGY, 2008, 33 (07) :1622-1628
[9]   A snapshot of renewable energy research in Turkey [J].
Celiktas, Melih Soner ;
Sevgili, Tarkan ;
Kocar, Gunnur .
RENEWABLE ENERGY, 2009, 34 (06) :1479-1486
[10]  
Dombayci OA, 2009, RENEW ENERG, V34, P1158, DOI [10.1016/j.renene.2008.07.007, 10.1016/j.renene.2011.03.030]