Advanced SearchSearch Tips
Adaptive Wavelet Neural Network Based Wind Speed Forecasting Studies
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Adaptive Wavelet Neural Network Based Wind Speed Forecasting Studies
Chandra, D. Rakesh; Kumari, Matam Sailaja; Sydulu, Maheswarapu; Grimaccia, F.; Mussetta, M.;
  PDF(new window)
Wind has been a rapidly growing renewable power source for the last twenty years. Since wind behavior is chaotic in nature, its forecasting is not easy. At the same time, developing an accurate forecasting method is essential when wind farms are integrated into the power grid. In fact, wind speed forecasting tools can solve issues related to grid stability and reserve allocation. In this paper 30 hours ahead wind speed profile forecast is proposed using Adaptive Wavelet Neural Network (AWNN). The implemented AWNN uses a Mexican hat mother Wavelet, and Morlet Mother Wavelet for seven, eight and nine levels decompositions. For wind speed forecasting, the time series data on wind speed has been gathered from the National Renewable Energy Laboratory (NREL) website. In this work, hourly averaged 10-min wind speed data sets for the year 2004 in the Midwest ISO region (site number 7263) is taken for analysis. Data sets are normalized in the range of [-1, 1] to improve the training performance of forecasting models. Total 8760 samples were taken for this forecasting analysis. After the forecasting phase, statistical parameters are calculated to evaluate system accuracy, comparing different configurations.
Wind speed forecasting;Adaptive Wavelet Neural Network (AWNN);Mexican hat wavelet;Morlet wavelet;Statistical parameters;
 Cited by
Hourly Average Wind Speed Simulation and Forecast Based on ARMA Model in Jeju Island, Korea,;;;

Journal of Electrical Engineering and Technology, 2016. vol.11. 6, pp.1548-1555 crossref(new window)
Song Jia, "A New Method for The Short-term Wind Speed Forecasting", 4th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies (DRPT), 2011, pp. 1320-1324.

Guoqiang Zhang, B. Eddy Patuwo, Michael Y. Hu "Forecasting With artificial neural networks: The state of the art" International Journal of forecasting, vol. 14, 1998, pp. 35-62. crossref(new window)

Liang Wu, Jeongje Park, Jaeseok Choi, Junmin Cha, Lee, K.Y., "A study on wind speed prediction using artificialneural network at Jeju Island in Korea", Transmission & Distribution Conference & Exposition, 2009, pp. 1-4.

T. Barbounis, J. Theocharis, M. Alexiadis, and P. Dokopoulos, "Longterm wind speed and power forecasting using local recurrent neural network models," IEEE Trans. Energy Convers., vol. 21, no. 1, pp. 273-284, Mar. 2006. crossref(new window)

Han Shuang, Liu Yongqian, Yang Yongping, "Taboo Search Algorithm Based ANN Model for Wind Speed Prediction," 2nd IEEE Conference on Industrial Electronics and Applications, 2007. ICIEA 2007.

Potter C.W., Negnevitsky.M, "Very short-term wind forecasting for Tasmanianpower generation", IEEE Transactions on Power Systems, Volume: 21, Issue: 2, 2006, pp. 965-972 crossref(new window)

Catalao J. P. S., Pousinho H. M. I., Mendes V. M. F., "Hybrid wavelet-PSO-ANFIS approach for shorttermwind power forecasting in Portugal", IEEE Transactions on Sustainable Energy, Volume: 2, Issue: 1, 2011, pp. 50-59

D. Rakesh Chandra, M. Sailaja Kumari, Sydulu. M, "A detailed literature review on wind forecasting", International Conference on Power, Energy and Control (ICPEC), 2013, pp. 630-634.

Gomes, P.; Castro, R., "Comparison of statistical wind speed forecastingmodels", World Congress on Sustainable Technologies (WCST), 2011, pp. 56-61.

Xing-Jie Liu ; Zeng-Qiang Mi ; Bai lu ; Wu Tao, "A Novel Approach for Wind Speed Forecasting Basedon EMD and Time-Series Analysis" Power and Energy Engineering Conference, 2009, pp. 1-4.

Khan, A.A.; Shahidehpour, M. "One day ahead wind speed forecasting using wavelets" Power Systems Conference and Exposition, 2009, pp. 1-5

Bhaskar. K., Singh. S. N., "AWNN-Assisted Wind Power Forecasting Using Feed-Forward Neural Network", IEEE Transactions on Sustainable Energy, Vol: 3, Issue: 2, 2012, pp. 306-315. crossref(new window)

National Renewable Energy Laboratory [Online].


Pindoriya. N. M, Singh. S. N, Singh. S. K., "An Adaptive Wavelet Neural Network-Based Energy Price Forecasting in Electricity Markets", IEEE Transactions On Power Systems, Vol. 23, No. 3, August 2008, pp. 1423-1432. crossref(new window)

Eduardo Martin Moraud, "Wavelet Networks" A report, 2009.

Mudathir Funsho Akorede, Hashim Hizam, "Wavelet Transforms: Practical Application in Power Systems", Journal of Electrical Engineering & Technology, Vol. 4, No. 2, pp. 168-174, 2009. crossref(new window)

Andrew Kusiak, Haiyang Zheng, and Zhe Song, "Short-Term Prediction of Wind Farm Power: A Data Mining Approach", IEEE Transactions On Energy Conversion, Vol. 24, No. 1, March 2009, pp. 125-136. crossref(new window)

George Sideratos and Nikos D. Hatziargyriou, "An Advanced Statistical Method for Wind Power Forecasting", IEEE Transactions on Power Systems, Vol. 22, No. 1, FEBRUARY 2007, pp. 258-265. crossref(new window)

Ramesh Babu.N, Arumozhivarman. P, "Improving Forecast Accuracy of Wind Speed Using Wavelet Transform and Neural Networks", Journal of Electrical Engineering & Technology, Vol. 8, No. 3, pp. 559-564, 2013. crossref(new window)

Yudun Li, Kaigui Xui, Bo Hu, "Copula-ARIMA Model for Multivariate Wind Speed and Its Application in reliability Assessment of Generating Systems", Journal of Electrical Engineering & Technology, Vol. 4, No. 2, pp. 168-174, 2009. crossref(new window)