Short-term wind speed forecasting model for wind farm based on wavelet decomposition

Accurate wind speed forecasting of wind farms can relieve or avoid the disadvantageous impact to the electric network. Wind speed forecasting therefore is necessary for power system because of the intermittence nature of wind. A lot of studies have been performed to develop the precision of wind speed prediction. In this paper, wavelet theory is described to decompose highly nonlinear wind speed time series into several approximate stationary time series. In terms of decomposed time series, different ARMA models are established respectively, and then, last forecasting results can be obtained by hybrid method. In order to test this approach, actual wind speed data from a weather station was used to establish forecasting model. The results indicate that wavelet theory is a useful tool in wind speed forecasting and possesses certain actual value.