Seçkin KARASU, Aytaç ALTAN, Zehra SARAÇ, Rıfat HACIOĞLU

ESTIMATION OF FAST VARIED WIND SPEED BASED ON NARX NEURAL NETWORK BY USING CURVE FITTING

In this study, a Nonlinear AutoRegressive eXogenous (NARX) neural network is used to estimate the wind speed on three monthly data sets taken from the wind central in Zonguldak province in Turkey. In the estimation study, the first and second order curve fitting coefficients of the measured temperature, pressure, humidity and solar radiation parameters together with the wind speed are used. In the estimation process, before these coefficients are applied directly to the NARX network structure, the most suitable features are selected with ReliefF method to minimize the MSE value. The number of delay steps in the NARX network structure is varied from 3 to 15 and the number of hidden neurons is varied from 3 to 15 to obtain model parameters that give the least estimation error. In order to determine the performance of the obtained model, the model is evaluated in terms of statistical error criteria such as MAE, MSE and RMSE. The model parameters and features matrix giving the least estimation error for the wind speed estimation of the NARX network structure are determined. It has been observed that this approach provides a high performance for estimating the wind speed with related to other measured parameters.

Keywords:

Wind speed Estimation, NARX, Curve fitting, ReliefF method,

PDF

___

[1] Koc, E., Senel, M.C., 2013, “Dünyada ve Türkiye’de enerji durumu-genel değerlendirme”, Mühendis ve Makina, 54(639), 32-44.
[2] Ozgener, O., 2002, “Türkiye’de ve Dünya’da rüzgar enerjisi kullanımı”, DEÜ Mühendislik Fakültesi Fen ve Mühendislik Dergisi, 4(3), 159-173.
[3] Tascikaraoglu, A., Uzunoglu, M., 2011, “Dalgacık dönüşümü ve yapay sinir ağları ile rüzgar hızı tahmini”, Elektrik-Elektronik ve Bilgisayar Sempozyumu, 106-111, Elazığ, Turkey.
[4] Senel, M.C., Koc, E., 2015, “Dünyada ve Türkiye’de rüzgâr enerjisi durumu-genel değerlendirme”, Mühendis ve Makina, 56(663), 46-56.
[5] Kose, B., Recebli, Z., Ozkaymak, M., 2014, “Stokastik modellerle rüzgâr hızı tahmini; Karabük örneği”, International Symposium on Innovative Technologies in Engineering and Science (ISITES), 18-20 June, 806-815, Karabuk, Turkey.
[6] Cakır, M.T., 2010, “Türkiye’nin rüzgâr enerji potansiyeli ve AB ülkeleri içindeki yeri”, Politeknik Dergisi, 13(4), 287-293.
[7] Ramasamy, P., Chandel, S.S., Yadav, A.K., 2015, “Wind speed prediction in the mountainous region of India using an artificial neural network model”, Renewable Energy, 80, 338-347.
[8] Chen, K., Yu, J., 2014, “Short-term wind speed prediction using an unscented Kalman filter based state-space support vector regression approach”, Applied Energy, 113, 690-705.
[9] Salcedo-Sanz, S., Pastor-Sanchez, A., Prieto, L., Blanco-Aguilera, A., Garcia-Herrera, R., 2014, “Feature selection in wind speed prediction systems based on a hybrid coral reefs optimization–extreme learning machine approach”, Energy Conversion and Management, 87, 10-18.
[10] Mori, H., Umezawa, Y., 2009, “Application of NBTree to selection of meteorological variables in wind speed prediction”, IEEE Transmission & Distribution Conference & Exposition: Asia and Pacific, 26-30 October, 1-4, Seoul, South Korea.
[11] Cadenas, E., Rivera, W., Campos-Amezcua, R., Heard, C., 2016, “Wind speed prediction using a univariate ARIMA model and a multivariate NARX model”, Energies, 9(2), 109-123.
[12] Mohandes, M.A., Halawani, T.O., Rehman, S., Hussain, A.A., 2004, “Support vector machines for wind speed prediction”, Renewable Energy, 29(6), 939-947.
[13] Robnik-Sikonja, M., Kononenko, I., 1997, “An adaptation of Relief for attribute estimation in regression”, Machine Learning: Proceedings of the Fourteenth International Conference (ICML’ 97), 296-304.
[14] Mporas, I., Ganchev, T., 2009, “Estimation of unknown speaker’s height from speech”, International Journal of Speech Technoogy, 12(4), 149-160.
[15] Kotsiantis, S., Koumanakos, E., Tzelepis, D., Tampakas,V., 2006, “Forecasting fraudulent financial statements using data mining”, International Journal Computational Intelligence, 3(2), 104-110.
[16] Amjady, N., Daraeepour, A., Keynia, F., 2010, “Day-ahead electricity price forecasting by modified relief algorithm and hybrid neural network”, IET Generation, Transmission & Distribution, 4(3), 432-444.
[17] Hyndman R.J., Koehler, A.B., 2006, “Another look at measures of forecast accuracy”, International Journal of Forecasting, 22(4), 679-688.