Wavelet Decomposition and Neuro-Fuzzy Hybrid System Applied to Short-Term Wind Power Forecasting

L.A. Fernandez-Jimenez, I.J. Ramirez-Rosado, B. Abebe, and M. Mendoza-Villena (Spain)


Shortterm wind power forecasting, wavelets, multiresolution analysis, neurofuzzy systems, Takagi Sugeno fuzzy systems (TSK)


This paper presents a new statistical short-term wind power forecasting model based on wavelet decomposition and neuro-fuzzy systems optimized with a genetic algorithm. The forecasted variable is the mean electric power production in a wind farm corresponding to half hour intervals. The forecasting horizons range from 0.5 to 4 hours. The optimization process, ruled by the genetic algorithm, selects the proper inputs and the parameters needed by a clustering algorithm to obtain after training, the neuro-fuzzy system with the lowest forecasting errors. The forecasting results obtained with the final models have been compared to those obtained with traditional forecasting models showing a better performance for all the forecasting horizons.

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