Wind Speed Prediction Model Based on Improved VMD and Sudden Change of Wind Speed

An accurate wind speed prediction system is of great importance prerequisite for realizing power grid integration and ensuring the safety system. Quantifying fluctuations can better provide valuable information dispatching. Therefore, this paper proposes a deterministic an interval method based on Lorentzian disturbance sequence. For forecasting, variational modal decomposition algorithm first used to reduce noise. The preprocessed data are then predicted by long short-term neural network, but there significant one-step lag in results. In response such limitation, slope introduced revise preliminary results, final model obtained. prediction, basis Lorenz theory describe dynamic atmospheric B-spline interpolation fit distribution series obtain experimental results show that proposed achieve higher forecasting accuracy than benchmark model, sequence ground truth coverage rate when average diameter small through fitting.