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

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. 

Keywords

• Wind Power,Prediction,Artificial Neuron Network

References

1. 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.
2. F. O. Hocaoğlu, M. Kurban, and Ü. B. Filik, "Wasp Yazılımı ile Rüzgar Potansiyeli Analizi ve Uygulama, IV," Yenilenebilir Enerji Kaynakları Sempozyumu, 2007.
3. 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.
4. 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.
5. R. Velo, P. López, and F. Maseda, “Wind speed estimation using multilayer perceptron,” Energy Conversion and Management, vol. 81, pp. 1-9, May. 2014.
6. 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.
7. G. Li, and J. Shi, “On comparing three artificial neural networks for wind speed forecasting,” Applied Energy, vol. 87, pp. 2313-2320, July. 2010.
8. 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.

9. 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.
10. 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.
11. 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.
12. 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.
13. 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.
14. 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.
15. Ç. Elmas, Yapay Zeka Uygulamaları, 2 nd ed., Ed. Ankara, Türkiye: Seçkin Yayıncılık, 2010.
16. C.D. Lewis, Industrial And Business Forecasting Methods, vol. 2, iss. 2, Ed. Borough Green, Sevenoaks, pp. 144, 1982.