Short-Term Wind Power Prediction Based on EMD-KPCA-LSTM

To enhance wind power prediction accuracy and guarantee smooth dispatch and secure grid system operation, this article proposes a wind power forecast model on the basis of EMD-KPCA-LSTM. Initially, the environmental factors are separated into Intrinsic Mode Functions (IMFs) and residual components through Empirical Mode Decomposition (EMD) to decrease their non-stationarity and complexity. Subsequently, the dimensionality of the model input is diminished through Kernel Principal Component Analysis (KPCA). Finally, wind power forecast is accomplished by utilizing a Long Short-Term Memory network (LSTM) to model the multivariate feature sequences. Using data from a wind farm in Xinjiang as a case study and comparing and analyzing it with the LSTM and EMD-LSTM prediction models, the findings demonstrate that the methodology put forward in this paper has exceptional predictive accuracy and applicability for short-term wind power predicting.