Estimation of solar radiation using artificial neural networks with different input parameters for Mediterranean region of Anatolia in Turkey
Estimation of solar radiation using artificial neural networks with different input parameters for Mediterranean region of Anatolia in Turkey
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DOI：
10.1016/j.eswa.2011.01.085
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发表时间：
2011-07
期刊：
影响因子：
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通讯作者：
A. Koca;H. Öztop;Y. Varol;G. Koca
中科院分区：
文献类型：
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作者：
A. Koca;H. Öztop;Y. Varol;G. Koca
利用人工神经网络模型对土耳其安纳托利亚地中海地区7个城市的太阳辐射参数进行了估算。众所周知，土耳其是亚洲和欧洲之间的桥梁，位于北纬36°至42°之间的阳光地带。事实上，该国有足够的太阳能辐射强度用于太阳能应用。为了估计太阳辐射，使用了土耳其国家和气象局的数据。2006年的数据用于测试，2005年，2007年和2008年的数据进行了估计。以太阳辐射作为输出层，考察了输入参数个数对太阳辐射的影响。为此，输入层参数的数量从2变为6。结果表明，该方法可用于研究人员或科学家设计高效率的太阳能器件。研究还发现，输入参数的数量是估计未来太阳辐射数据的最有效参数。
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° and 42°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.