Wavelet Based Spike Propagation Neural Network (WSPNN) for Wind Power Forecasting

Smitha Elsa Peter(1*), Santosh Kulkarni(2), I. Jacob Raglend(3), Sishaj P. Simon(4)

(1) PRIST University, Thanjavur, India
(2) Jawaharlal Nehru Technological University Hyderabad and National Institute of Technology Tiruchirappalli, India
(3) of Electrical Sciences, Noorul Islam University, Kumaracoil, Kanyakumari, Tamil Nadu, India
(4) Institute of Technology (NIT) (formerly Regional Engineering College), Tiruchirappalli, Tamil Nadu, India
(*) Corresponding author

DOI's assignment:
the author of the article can submit here a request for assignment of a DOI number to this resource!
Cost of the service: euros 10,00 (for a DOI)


The development of wind power generation in many countries around the world in recent years has drawn the attention of research scientists to forecast wind power. The characteristics of the wind power generated are extremely variable, non-periodic and unpredictable. Therefore, wind power forecasts have gained a lot of prominence and its significance in turbine control, pre-load sharing and trading of power. Based on the literature survey, though various wind power forecasting models have been proposed, scope for developing much more efficient and accurate models is still a major challenge. This work proposes a wavelet based spike propagation neural network for wind power forecasting. First the preprocessing of historical data is carried out using wavelet technique.  Then, the third generation artificial neural networks, that involve the spike response neuron model (SRNM) is trained using the gradient descent approach. The proposed model is tested on the historical wind power data obtained from Irish power grid and National Renewable Energy Laboratory (USA) website. The performance of the proposed model is validated based on the indices such as modified mean absolute percentage error (MMAPE), root mean square error (RMSE) and error variance.
Copyright © 2013 Praise Worthy Prize - All rights reserved.


Artificial Neural Networks; Spike Response Model (SRM); Spiking Neurons; Wind Power forecasting (WPF)

Full Text:



European Union (EU), Climate change Commission welcomes final adoption of Europe’s climate and energy package, Press Release, EU, Dec.17th2008. Availableat:http://eurpa.eu/rapid/pressReleasesAction.do?reference=IP/08/1988.

A. Arulampalam, M. Barnes, N. Jenkins, J.B. Ekanayake, Power quality and stability improvement of a wind farm using STATCOM supported with hybrid battery energy storage, IEEE Proceedings - Generation, Transmission and Distribution, Vol. 153, 2006, p. 701-710.

D. Karlsson, M. Hemmingsson, S. Lindahl, Wide area system monitoring and control - terminology, phenomena, and solution implementation strategies, IEEE Power and Energy Magazine, Vol. 2, 2004, pp. 68-76.

M.R. Patel, Wind and Solar Power Systems - Design Analysis and Operation, (CRC Press, 2005).

Nima Amjady, Farshid Keynia and Hamidreza Zareipour, Short term wind forecasting using Ridgelet neural network, Electrical Power Systems Research, Vol. 81, pp.2099-2107, 2011.

M. Lei, L. Shiyan, J. Chuanwen, L. Hongling, Z. Yan, A review on the forecasting of wind speed and generated power, Renew. Sustain. Energy Rev. Vol. 13 (Issue 4) (2009), 915–920.

A. Costa, A. Crespo, J. Navarro, G. Lizcano, H. Madsen, E. Feitosa, A review on the young history of the wind power short-term prediction, Renew. Sustain. Energy Rev. Vol. 12 (Issue 6) (2008) 1725–1744.

L. Ma, S. Y. Luan, C. W. Jiang, H. L. Liu, and Y. Zhang, A review on the forecasting of wind speed and generated power, Renew. Sust.Energy Rev., Vol. 13, (Issue 4), pp. 915–920, May 2009.

T. H. M. El-Fouly, E. F. El-Saadany, and M. M. A. Salama, One day ahead prediction of wind speed and direction, IEEE Trans. Energy Convers., Vol. 23, (Issue 1), pp. 191–201, Mar. 2008.

R. G. Kavasseri and K. Seetharaman, Day-ahead wind speed forecasting using f-ARIMA models, Renew. Energy, Vol. 34, (Issue 5), pp. 1388–1393, May 2009.

G. N. Kariniotakis, G. S. Stavrakakis, and E. F. Nogaret, Wind power forecasting using advanced neural network models, IEEE Trans. Energy Convers.,Vol. 11,(Issue 4), pp. 762–767, Dec. 1996.

S. Li, D. C. Wunsch, E. A. O’Hair, and M. G. Giesselmann, Using neural networks to estimate wind turbine power generation, IEEE Trans. Energy Convers., Vol. 16, (Issue 3), pp. 276–282, Sep. 2001.

Ramesh Babu, N., Arulmozhivarman, P., Forecasting of wind speed using artificial neural networks, (2012) International Review on Modelling and Simulations (IREMOS), 5 (5), pp. 2276-2280.

J.P.S. Catalao, H.M.I. Pousinho, V.M.F. Mendes, Short-term wind power forecasting in Portugal by neural networks and wavelet transform, Renew. Energy Vol. 36 (Issue 4), pp. 1245–1251, 2011.

I. G. Damousis, M. C. Alexiadis, J. B. Therocharis, and P. S. Dokopoulos, A fuzzy model for wind speed prediction and power generation in wind parks using spatial correlation, IEEE Trans. Energy Convers., Vol. 19, (Issue 2), pp. 352–361, Jun. 2004.

G. Sideratos and N. D. Hatziargyriou, An advanced statistical method for wind power forecasting, IEEE Trans. Power Syst., Vol. 22, (Issue 1), pp. 258–265, Feb. 2007.

R. Jursa and K. Rohrig, Short-term wind power forecasting using evolutionary algorithms for the automated specification of artificial intelligence models, Int. J. Forecast., Vol. 24, (Issue 4), pp. 694–709, Oct./Dec. 2008.

Abroshan, M., Mahdi Mousavi Sangdehi, S., Torabi, K., Goodarzi, M., Individual Particle Optimization algorithm for linear forecasting of wind speed, (2013) International Review of Electrical Engineering (IREE), 8 (1), pp. 297-304.

S. Fan, J. R. Liao, R. Yokoyama, L. N. Chen, and W. J. Lee, Forecasting the wind generation using a two-stage network based on meteorological information, IEEE Trans. Energy Convers., Vol. 24, (Issue 2), pp. 474–482, Jun. 2009.

R. J. Bessa, V. Miranda, and J. Gama, Entropy and correntropy against minimum square error in offline and online three-day ahead wind power forecasting,, IEEE Trans. Power Syst., Vol. 24, (Issue 4), pp. 1657–1666, Nov. 2009.

L. Hodgkin and A. F. Huxley, A quantitative description of ion currents and its applications to conductance and excitation in nerve membranes, J. Physiol. (London), Vol. 117, pp. 500–544, 1952.

P. Joshi and W. Maass, Movement generation with circuits of spiking neurons, Neural Computation., Vol. 17, (Issue 8), pp. 1715–1738, 2005.

Wulfarm Gelsterner, Spiking Neuron Models, (Cambridge University Press, 2002).

Gerstner W. Time structure of the activity in neural network models, Phys. Rev. E, Vol. 51, pp.738-758, 1995.

V. Sharma. D. Srinivasan, A Spiking Neural Network Based on Temporal Encoding for Electricity Price Time Series Forecasting in Deregulated Markets, Neural Networks International Conference (IJCNN), July 2010.

S.M. Bohte, H. La. Poutre and J. N. Kok, Spike-Prop: Error Backpropagation in Multi-Layer Networks of Spiking Neurons, Neuro Computing, 48(No. 1-4), pp. 7-37, November 2002.



D. Benaouda , F.Mmurtagh , J.L.Sstarck , and O.Rrenaud, Wavelet-Based Nonlinear Multiscale Decomposition Model for Electricity Load Forecasting, Neurocomputing IJON, Vol. 70, (No. 1-3), pp. 139-154, 2006.

A. J. R. Reis and A. P. A. Da Silva, Feature extraction via multiresolution analysis for short-term load forecasting, IEEE Trans. Power Syst., Vol. 20, (Issue 1), pp. 189–198, Feb. 2005.

J. P. S. Catalão, H. M. I. Pousinho and V. M. F. Mendes, Hybrid Wavelet-PSO-ANFIS Approach for Short-Term Wind Power Forecasting in Portugal, IEEE Transactions on Sustainable Energy, Vol. 2, (Issue 1), January 2011.

A. J. R. Reis and A. P. A. Da Silva, Feature extraction via multiresolution analysis for short-term load forecasting, IEEE Trans. Power Syst., Vol. 20, (Issue 1), pp. 189–198, Feb. 2005.

A. J. Conejo, M. A. Plazas, R. Espínola, and A. B. Molina, Day-ahead electricity price forecasting using the wavelet transforms and ARIMA models, IEEE Trans. Power Syst., Vol. 20, (Issue 2), pp. 1035–1042, May 2005.

Box, G. E. P., G. M. Jenkins, and G. C. Reinsel, Time Series Analysis: Forecasting and Control,( Prentice-Hall: Upper Saddle River, NJ, 1994,3rd ed.).

Mohseni-Mansur, S.M., Pirayesh, A., Abdollahi-Mansoorkhani, H.R., Abedini-Duki, E., A multi objective framework for solving economic load dispatch problem including stochastic nature of wind power, (2013) International Review of Electrical Engineering (IREE), 8 (1), pp. 362-368.

Li, Y., Gu, X.-P., Application of online SVR in very short-term load forecasting, (2013) International Review of Electrical Engineering (IREE), 8 (1), pp. 277-283.

Farahat, M.A., Talaat, M., The using of curve fitting prediction optimized by genetic algorithms for short-term load forecasting, (2012) International Review of Electrical Engineering (IREE), 7 (6), pp. 6209-6215.

Porkar, S., Poure, P., Saadate, S., Distributed generation electricity price forecasting in a deregulated electricity market, (2012) International Review of Electrical Engineering (IREE), 7 (5), pp. 5829-5839.

Behi, T., Arous, N., Ellouze, N., Comparative study of SOM variants in recurrent pulsed neural networks case study: Phoneme and word recognition on the TIMIT speech corpus, (2012) International Review on Computers and Software (IRECOS), 7 (6), pp. 3184-3194.


  • There are currently no refbacks.

Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2022 Praise Worthy Prize