Abstract
Wind power forecasting is the major area of concern in wind power generation due to the unpredictability of wind speed. Existing soft computing and statistical methods focus towards deterministic wind forecasting and neglects the uncertainties associated with wind flow. In this paper, the wavelet decomposition combined with adaptive neuro fuzzy inference system is used for short term wind power forecasting. Wavelet decomposition handles the wind power data series fluctuations and the neuro fuzzy system ensures fast learning in a real time scenario. Support vector machine (SVM) is utilized to reduce the prediction errors. A quantile regression has been applied on the SVM output to improve accuracy. The proposed forecasting method is assessed using the real time data collected from the Coimbatore wind farm, India. The assessment is conducted in different seasons to reduce the errors and improve the prediction accuracy.
This is a preview of subscription content, log in via an institution to check access.
References
Fan S, Liao JR, Yokoyama R, Chen L, Lee W-J (2009) Forecasting the wind generation using a two-stage network based on meteorological information. IEEE Trans Energy Convers 24(2):474–482
Lange M (2005) On the uncertainty of wind power predictions analysis of the forecast accuracy and statistical distribution of errors. J Sol Energy Eng 127(2):177–194
Taylor J, McSharry P (2007) Short-term load forecasting methods: an evaluation based on European data. IEEE Trans Power Syst 22(4):2213–2219
Petersen EL, Mortensen NG, Landberg L, Højstrup J, Frank HP (1997) Wind power meteorology. Risø-I-1206(EN). Risø National Laboratory, Roskilde
Giebel G (2011) The state of the art in short-term prediction of wind power: a literature overview. Deliverable 1.2b of the ANEMOS.plus project, vol 2
Torres JL, Garcia A, De Blas M, De Francisco A (2005) Forecast of hourly average wind speed with ARMA models in Navarre (Spain). Sol Energy 79:65–77
Amjady N, Keynia F, Zareipour H (2011) Wind power prediction by a new forecast engine composed of modified hybrid neural network and enhanced particle swarm optimization. IEEE Trans Sustain Energy 2(3):265–276
Haque AU, Meng J (2011) Short-term wind speed forecasting based on fuzzy ARTMAP. Int J Green Energy 8(1):65–80
Mohandes M, Halawani T, Rehman S, Hussain AA (2004) Support vector machines for wind speed prediction. Renew Energy 29(6):939–947
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Ranganayaki, V., Deepa, S.N. SVM Based Neuro Fuzzy Model for Short Term Wind Power Forecasting. Natl. Acad. Sci. Lett. 40, 131–134 (2017). https://doi.org/10.1007/s40009-016-0521-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40009-016-0521-6