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Wind Power Forecasting For The Province Of Osmaniye Using Artificial Neural Network Method

Year 2016, Volume: 4 Issue: Special Issue-1, 114 - 117, 26.12.2016

Abstract

Although wind energy at certain intervals and
random in nature, today it is one of the commonly utilized alternative energy
source in the world. Because of sustainability and environmentally-friendly
energy source, countries increasingly benefit from wind energy. Several
estimation methods are applied in the determination of a region's wind energy
potential. Today, one of the most commonly used prediction methods is
artificial neural network (ANN) method. In this study, Estimation of wind power
in Osmaniye district was investigated in method with artificial neural network
(ANN) using data from meteorological measurement stations from the
meteorological measurement device at the campus of Osmaniye Korkut ATA
University. In order to give the best values of prediction results, several
methods increasing the impact on output of different models for the input
variables were investigated. 

References

  • H. Mituharu, and B. Kermanshahi, "Application of artificial neural network for wind speed prediction and determination of wind power generation output," Proceedings of ICEE, 2001.
  • F. O. Hocaoğlu, M. Kurban, and Ü. B. Filik, "Wasp Yazılımı ile Rüzgar Potansiyeli Analizi ve Uygulama, IV," Yenilenebilir Enerji Kaynakları Sempozyumu, 2007.
  • Y. Noorollahi, M. A. Jokar and A. Kalhor, "Using artificial neural networks for temporal and spatial wind speed forecasting in Iran," Energy Conversion and Management, vol. 115, pp. 17-25, May. 2016.
  • M. Lei, L. Shiyan, J. Chuanwen, L. Hongling, and Z. Yan, “A review on the forecasting of wind speed and generated power,” Renewable and Sustainable Energy Reviews, vol. 13, pp. 915-920, May. 2009.
  • R. Velo, P. López, and F. Maseda, “Wind speed estimation using multilayer perceptron,” Energy Conversion and Management, vol. 81, pp. 1-9, May. 2014.
  • E. Cadenas, and W. Rivera, “Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA–ANN model,” Renewable Energy, vol. 35, pp. 2732-2738, December. 2010.
  • G. Li, and J. Shi, “On comparing three artificial neural networks for wind speed forecasting,” Applied Energy, vol. 87, pp. 2313-2320, July. 2010.
  • Z. W. Zhenga, Y. Y. Chena, X. W. Zhoua, M. M. Huoa, B. Zhaoc and M. Y. Guod, “Short-Term Wind Power Forecasting Using Empirical Mode Decomposition and RBFNN,” International Journal of Smart Grid and Clean Energy, vol. 2, pp. 192–199, May. 2013.
  • S. Wang, X. Liu, Y. Jin, and K. Qu, “Wind Power Short Term Forecasting based on Back Propagation Neural Network,” International Journal of Smart Home, vol. 9, pp. 231-240, 2015.
  • M. C. Mabel, and E. Fernandez, “Analysis of wind power generation and prediction using ANN: A case study,” Renewable Energy, vol. 33, pp. 986-99, May. 2008.
  • M. Bilgili, B. Sahin, and A. Yasar, “Application of artificial neural networks for the wind speed prediction of target station using reference stations data,” Renewable Energy, vol. 32, pp. 2350-2360, Nov. 2007.
  • S. Tasdemir, I. Saritas, M. Ciniviz and N. Allahverdi, "Artificial Neural Network and Fuzzy Expert System Comparison for Prediction of Performance and Emission Parameters on a Gasoline Engine," Expert Systems with Applications (ISI), vol. 29, pp. 1471-1480, 2012.
  • B. Yaniktepe, and Y.A. Genc, “Establishing new model for predicting the global solar radiation on horizontal surface,” International Journal of Hydrogen Energy, vol. 40, pp. 15278-15283, Nov. 2015.
  • S. Yavuz, and M. Deveci, "İstatiksel Normalizasyon Tekniklerinin Yapay Sinir Ağin Performansina Etkisi," Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, vol. 40, pp. 167-187, 2012.
  • Ç. Elmas, Yapay Zeka Uygulamaları, 2 nd ed., Ed. Ankara, Türkiye: Seçkin Yayıncılık, 2010.
  • C.D. Lewis, Industrial And Business Forecasting Methods, vol. 2, iss. 2, Ed. Borough Green, Sevenoaks, pp. 144, 1982.
Year 2016, Volume: 4 Issue: Special Issue-1, 114 - 117, 26.12.2016

Abstract

References

  • H. Mituharu, and B. Kermanshahi, "Application of artificial neural network for wind speed prediction and determination of wind power generation output," Proceedings of ICEE, 2001.
  • F. O. Hocaoğlu, M. Kurban, and Ü. B. Filik, "Wasp Yazılımı ile Rüzgar Potansiyeli Analizi ve Uygulama, IV," Yenilenebilir Enerji Kaynakları Sempozyumu, 2007.
  • Y. Noorollahi, M. A. Jokar and A. Kalhor, "Using artificial neural networks for temporal and spatial wind speed forecasting in Iran," Energy Conversion and Management, vol. 115, pp. 17-25, May. 2016.
  • M. Lei, L. Shiyan, J. Chuanwen, L. Hongling, and Z. Yan, “A review on the forecasting of wind speed and generated power,” Renewable and Sustainable Energy Reviews, vol. 13, pp. 915-920, May. 2009.
  • R. Velo, P. López, and F. Maseda, “Wind speed estimation using multilayer perceptron,” Energy Conversion and Management, vol. 81, pp. 1-9, May. 2014.
  • E. Cadenas, and W. Rivera, “Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA–ANN model,” Renewable Energy, vol. 35, pp. 2732-2738, December. 2010.
  • G. Li, and J. Shi, “On comparing three artificial neural networks for wind speed forecasting,” Applied Energy, vol. 87, pp. 2313-2320, July. 2010.
  • Z. W. Zhenga, Y. Y. Chena, X. W. Zhoua, M. M. Huoa, B. Zhaoc and M. Y. Guod, “Short-Term Wind Power Forecasting Using Empirical Mode Decomposition and RBFNN,” International Journal of Smart Grid and Clean Energy, vol. 2, pp. 192–199, May. 2013.
  • S. Wang, X. Liu, Y. Jin, and K. Qu, “Wind Power Short Term Forecasting based on Back Propagation Neural Network,” International Journal of Smart Home, vol. 9, pp. 231-240, 2015.
  • M. C. Mabel, and E. Fernandez, “Analysis of wind power generation and prediction using ANN: A case study,” Renewable Energy, vol. 33, pp. 986-99, May. 2008.
  • M. Bilgili, B. Sahin, and A. Yasar, “Application of artificial neural networks for the wind speed prediction of target station using reference stations data,” Renewable Energy, vol. 32, pp. 2350-2360, Nov. 2007.
  • S. Tasdemir, I. Saritas, M. Ciniviz and N. Allahverdi, "Artificial Neural Network and Fuzzy Expert System Comparison for Prediction of Performance and Emission Parameters on a Gasoline Engine," Expert Systems with Applications (ISI), vol. 29, pp. 1471-1480, 2012.
  • B. Yaniktepe, and Y.A. Genc, “Establishing new model for predicting the global solar radiation on horizontal surface,” International Journal of Hydrogen Energy, vol. 40, pp. 15278-15283, Nov. 2015.
  • S. Yavuz, and M. Deveci, "İstatiksel Normalizasyon Tekniklerinin Yapay Sinir Ağin Performansina Etkisi," Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, vol. 40, pp. 167-187, 2012.
  • Ç. Elmas, Yapay Zeka Uygulamaları, 2 nd ed., Ed. Ankara, Türkiye: Seçkin Yayıncılık, 2010.
  • C.D. Lewis, Industrial And Business Forecasting Methods, vol. 2, iss. 2, Ed. Borough Green, Sevenoaks, pp. 144, 1982.
There are 16 citations in total.

Details

Subjects Engineering
Journal Section Research Article
Authors

Bulent Yanıktepe

SAKIR Tasdemır

A. BURAK Guher

Sultan Akcan This is me

Publication Date December 26, 2016
Published in Issue Year 2016 Volume: 4 Issue: Special Issue-1

Cite

APA Yanıktepe, B., Tasdemır, S., Guher, A. B., Akcan, S. (2016). Wind Power Forecasting For The Province Of Osmaniye Using Artificial Neural Network Method. International Journal of Intelligent Systems and Applications in Engineering, 4(Special Issue-1), 114-117. https://doi.org/10.18201/ijisae.270560
AMA Yanıktepe B, Tasdemır S, Guher AB, Akcan S. Wind Power Forecasting For The Province Of Osmaniye Using Artificial Neural Network Method. International Journal of Intelligent Systems and Applications in Engineering. December 2016;4(Special Issue-1):114-117. doi:10.18201/ijisae.270560
Chicago Yanıktepe, Bulent, SAKIR Tasdemır, A. BURAK Guher, and Sultan Akcan. “Wind Power Forecasting For The Province Of Osmaniye Using Artificial Neural Network Method”. International Journal of Intelligent Systems and Applications in Engineering 4, no. Special Issue-1 (December 2016): 114-17. https://doi.org/10.18201/ijisae.270560.
EndNote Yanıktepe B, Tasdemır S, Guher AB, Akcan S (December 1, 2016) Wind Power Forecasting For The Province Of Osmaniye Using Artificial Neural Network Method. International Journal of Intelligent Systems and Applications in Engineering 4 Special Issue-1 114–117.
IEEE B. Yanıktepe, S. Tasdemır, A. B. Guher, and S. Akcan, “Wind Power Forecasting For The Province Of Osmaniye Using Artificial Neural Network Method”, International Journal of Intelligent Systems and Applications in Engineering, vol. 4, no. Special Issue-1, pp. 114–117, 2016, doi: 10.18201/ijisae.270560.
ISNAD Yanıktepe, Bulent et al. “Wind Power Forecasting For The Province Of Osmaniye Using Artificial Neural Network Method”. International Journal of Intelligent Systems and Applications in Engineering 4/Special Issue-1 (December 2016), 114-117. https://doi.org/10.18201/ijisae.270560.
JAMA Yanıktepe B, Tasdemır S, Guher AB, Akcan S. Wind Power Forecasting For The Province Of Osmaniye Using Artificial Neural Network Method. International Journal of Intelligent Systems and Applications in Engineering. 2016;4:114–117.
MLA Yanıktepe, Bulent et al. “Wind Power Forecasting For The Province Of Osmaniye Using Artificial Neural Network Method”. International Journal of Intelligent Systems and Applications in Engineering, vol. 4, no. Special Issue-1, 2016, pp. 114-7, doi:10.18201/ijisae.270560.
Vancouver Yanıktepe B, Tasdemır S, Guher AB, Akcan S. Wind Power Forecasting For The Province Of Osmaniye Using Artificial Neural Network Method. International Journal of Intelligent Systems and Applications in Engineering. 2016;4(Special Issue-1):114-7.