• DocumentCode
    3586645
  • Title

    Wind power estimation algorithm using artificial neural networks case study: Ereğli

  • Author

    Cetinkaya, Nurettin ; Yapici, Hamza

  • Author_Institution
    Dept. of Electr. Electron. Eng., Selcuk Univ., Konya, Turkey
  • fYear
    2014
  • Firstpage
    67
  • Lastpage
    72
  • Abstract
    By the global warming and decreasing fossil fuel, alternative energy sources are looked for future and protecting environment. In the recent years, many studies are made about wind power whereby deteriorating environment will be regarded. This study prefers artificial neural network (ANN) algorithm to estimate electrical energy output of wind turbines can be constructed. Although many environmental effects such as wind speed, air density or temperature influence wind turbines installation, ANN estimates electrical energy and power output in the minimum cost. The wind turbine parameters of three manufacturers have been chosen so as to train ANN. For the structure of ANN, 1 hidden layer and 26 neurons have been set. Data in this work have been measured at Ereğli terrain in Konya, Turkey. This daily data have been taken between January 2013 and February 2014.
  • Keywords
    environmental factors; neural nets; power engineering computing; wind power; wind turbines; ANN algorithm; Eregli; alternative energy sources; artificial neural networks; decreasing fossil fuel; global warming; wind power estimation algorithm; wind turbines; Artificial neural networks; Estimation; Training; Wind farms; Wind power generation; Wind speed; Wind turbines; ANN; annual electrical energy estimation; power plant structure; wind turbine;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Electronics, Computers and Artificial Intelligence (ECAI), 2014 6th International Conference on
  • Print_ISBN
    978-1-4799-5478-0
  • Type

    conf

  • DOI
    10.1109/ECAI.2014.7090183
  • Filename
    7090183