A short-term wind power prediction method based on dynamic and static feature fusion mining

Abstract

Wind power is a kind of time-varying time series with fluctuation characteristics. To take full advantage of the time-varying value provided by wind power fluctuations, a short-term wind power prediction method based on dynamic and static feature fusion mining is proposed. First, three statistical features are manually constructed to characterize the dynamic fluctuation of wind speed, these features provide more valuable patterns for the input data. Then, we construct a residual network structure that incorporates the bidirectional gate recurrent unit, and incorporate temporal and spatial attention mechanisms in the network structure. This network structure is used to train the wind power prediction model, which has great advantages in reducing the degradation and overfitting problems caused by increasing the depth of the network. Finally, a wind power prediction index is proposed to quantify the proportion of NWP link error and modeling link error in the total error. Simulation experiments were conducted on a wind farm with an installed capacity of 400.5 MW in Jilin Province, China, and the predicted NRMSE is 0.1581.