Advanced SearchSearch Tips
Improving Forecast Accuracy of Wind Speed Using Wavelet Transform and Neural Networks
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Improving Forecast Accuracy of Wind Speed Using Wavelet Transform and Neural Networks
Ramesh Babu, N.; Arulmozhivarman, P.;
  PDF(new window)
In this paper a new hybrid forecast method composed of wavelet transform and neural network is proposed to forecast the wind speed more accurately. In the field of wind energy research, accurate forecast of wind speed is a challenging task. This will influence the power system scheduling and the dynamic control of wind turbine. The wind data used here is measured at 15 minute time intervals. The performance is evaluated based on the metrics, namely, mean square error, mean absolute error, sum squared error of the proposed model and compared with the back propagation model. Simulation studies are carried out and it is reported that the proposed model outperforms the compared model based on the metrics used and conclusions were drawn appropriately.
Wind speed;Forecast;Wavelet transform;Neural networks;Back propagation;
 Cited by
Adaptive Wavelet Neural Network Based Wind Speed Forecasting Studies,Chandra, D. Rakesh;Kumari, Matam Sailaja;Sydulu, Maheswarapu;Grimaccia, F.;Mussetta, M.;

Journal of Electrical Engineering and Technology, 2014. vol.9. 6, pp.1812-1821 crossref(new window)
Adaptive Wavelet Neural Network Based Wind Speed Forecasting Studies, Journal of Electrical Engineering and Technology, 2014, 9, 6, 1812  crossref(new windwow)
Wavelet based ICA using maximisation of non-Gaussianity for acoustic echo cancellation during double talk situation, Applied Acoustics, 2015, 97, 37  crossref(new windwow)
Dynamic Neural Network Based Very Short-Term Wind Speed Forecasting, Wind Engineering, 2014, 38, 2, 121  crossref(new windwow)
Most influential parametrical and data needs for realistic wind speed prediction, Renewable Energy, 2016, 94, 452  crossref(new windwow)
Prediction of Wind Power by Chaos and BP Artificial Neural Networks Approach Based on Genetic Algorithm, Journal of Electrical Engineering and Technology, 2015, 10, 1, 41  crossref(new windwow)
Comparison of new hybrid FEEMD-MLP, FEEMD-ANFIS, Wavelet Packet-MLP and Wavelet Packet-ANFIS for wind speed predictions, Energy Conversion and Management, 2015, 89, 1  crossref(new windwow)
Haque A U, Mandal P, Kaye M E, Meng J, Chang L, Senjyu T. A new strategy for predicting short term wind speed using soft computing models. Renew Sust Energy Rev, Vol. 16, pp. 4563-4573, 2012. crossref(new window)

Ramesh Babu N, Arulmozhivarman P. Wind energy conversion systems - A technical review. J Eng Sci Tech, Vol. 8, No. 4, 2013.

Zhao P, Wang J, Xia J, Dai Y, Sheng Y, Yue J. Performance evaluation and accutacy enhancement of a day-ahead wind power forecasting system in China. Renew Energy, Vol. 43, pp. 234-241, 2012. crossref(new window)

Li S, Wunsch DC, O'Hair EA, Giesselmann MG. Using neural networks to estimate wind turbine power generation. IEEE Trans Energy Convers, Vol. 16, No. 3, 2001.

Mohandes MA, Halawani TO, Rehman S, Hussain AA. Support vector machines for wind speed predicttion. Renew Energy, Vol. 29, pp. 939-947, 2004. crossref(new window)

Flores P, Tapia A, Tapia G. Application of a control algorithm for wind speed prediction and active power generation. Renew Energy, Vol. 30, pp. 523-536, 2005. crossref(new window)

El-Fouly THM, El-Saadany EF, Salama MMA. One day ahead prediction of wind speed using annual trends. IEEE power engineering society general meeting, 2006.

Barbounis TG, Theocharis JB, Alexiadis MC, Dokopoulos PS. Long-term wind speed and power forecasting using local recurrent neural network models. IEEE Trans Energy Convers, Vol. 21, No. 1, pp. 273- 284, 2006. crossref(new window)

Pourmousavi kani S A, Ardehali M M. Very shortterm wind speed prediction: A new artificial neural network - Markov chain model. Energy Convers Manage, Vol. 52, pp. 738-745, 2011. crossref(new window)

Amjady N, Keynia F. Short-term load forecast of power systems by combination of wavelet transform and neuro-evolutionary algorithm. Energy, Vol. 34, pp. 46-57, 2009. crossref(new window)

Siwek K, Osowski S. Improving the accuracy of prediction of PM10 pollution by the wavelet transformation and an ensemble of neural predictors. Eng Appl Artif Intell, Vol. 25, pp. 1246-1258, 2012. crossref(new window)

Eynard J, Grieu S, Polit M. Wavelet-based multiresolution analysis and artificial neural networks for temperature and thermal power consumption. Engg Appl Artif Intell, Vol. 24, pp. 501-516, 2011. crossref(new window)

Mallat S. A wavelet tour of signal processing. Burlington : Academic Press, 2009, pp. 284-297.

Lei M, Shiyan L, Chuanwen J, Hongling L, Yan Z. A review on the forecasting of wind speed and generated power. Renew Sust Energy Rev, Vol. 13, pp. 915-920, 2009. crossref(new window)

Ramesh Babu N, Arulmozhivarman P. Forecasting of Wind Speed using Artificial Neural Networks. Int. Rev. Mod. Sim, Vol. 5 No. 5, 2012.

Philippopoulos K, Deligiorgi D. Application of artificial neural networks for the spatial estimation of wind speed in a coastal region with complex topography. Renew Energy, Vol. 38, pp. 75-82, 2012. crossref(new window)