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SETSCI - Volume 1 (2017)
ISMSIT2017 - International Symposium on Multidisciplinary Studies and Innovative Technologies, Tokat, Turkey, Dec 02, 2017

Forecasting the Installed Wind Power in Turkey by Artificial Neural Network
Mehmet Feyzi Özsoy1*, Hakan Aydoğan2
1Usak University, Uşak, Turkey
2Usak University, Uşak, Turkey
* Corresponding author: mehmetfeyzi.ozsoy@usak.edu.tr
Published Date: 2017-12-08   |   Page (s): 167-170   |    666     11
ABSTRACT The aim of this study is to forecast installed wind power which is a one of the renewable energy resources in Turkey for the year of 2017 based on artificial neural network method using the normalized last ten-year data. An artificial neural network has been carried out to forecast the installed wind power in Turkey. The artificial neural network created by Matlab software has been designed as the 3 inputs and one output, one hidden layer and feed forward back propagation properties at the end of the trial and error method of the training and simulation. The hidden layer has 100 neurons with tansig activation function and the output layer has single neuron with purelin activation function. The artificial neural network has been trained using the data consist of the installed wind power in Turkey between the years of 2005-2015. The training method has been chosen as the traingdm. The normalized sequential years of the installed wind power data have been applied to the inputs and the following year of the installed wind power data has been applied to the output. The installed wind power has been reached to 5751.3 MW by the end of 2016 in according to the TEIAS. In the scenario one appearing in the document of production capacity projection (2016-2020) published by the EMRA, the installed wind power has been forecasted by the absolute deviation of 7.54 % as 5317.3 MW by the year of 2016. This study has forecasted by the absolute deviation of 4.53 % as 6011.88 MW by the year of 2016 and the 6277.39 MW for the year of 2017.  
KEYWORDS renewable energy, wind, artificial neural network, forecast
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