Short-term wind speed forecasting based on non-stationary time series analysis and ARCH model

Wind speed time series is nonlinear and non-stationary, and has time-varying variance. Therefore, Wind speed is often considered as one of the most difficult meteorological parameters to forecast. The proposed model is based on non-stationary time series theory and ARCH model. First, wind speed series is decomposed and reconstructed into approximate series and detailed series by wavelet analysis. Then use ARIMA model to analyze each part, simultaneously considering the heteroscedasticity effect of the residual series, the corresponding ARIMA-ARCH model is set up. The final forecasting wind speed values are the sum of the predicted approximate and detailed values. This proposed method is applied to forecast the actual wind speed data and verification results show it can improve the accuracy of wind speed forecasting.