Applying Wavelet Filters in Wind Forecasting Methods

Wind is a physical phenomenon with uncertainties in several temporal scales, addition, measured wind time series have noise superimposed on them. These are the basis for forecasting methods. This paper studied application of wavelet transform to three methods, namely, stochastic, neural network, and fuzzy, six families. speed were first filtered eliminate high-frequency component using filters then different methods applied series. All showed important improvements when filter was applied. It note that technique requires deep study order select appropriate family level. The best results obtained an optimal filtering level improper selection may significantly affect accuracy results.