A wind power forecasting method based on optimized decomposition prediction and error correction

Abstract

To reduce the effect of nonlinearity and volatility in the wind power time sequence, a two-stage short-term wind power forecasting method based on optimized decomposition prediction and error correction is proposed. In the first stage, in order to improve the decomposition effect of variational mode decomposition (VMD), the decomposition loss is defined as the evaluation criterion to guide the parameter setting of VMD, and flower pollination algorithm (FPA) is utilized to automatically optimize the parameters of VMD. Then the complex wind power sequence is decomposed into simple intrinsic mode functions (IMFs). Besides, bi-directional long short-term memory (BiLSTM) neural network is built for each IMF to explore the deep time-series features of wind power in both past and future directions. In the second stage, to reduce the correlation among meteorological factors, principal component analysis (PCA) is employed to convert the multi-dimensional meteorological factors into low-dimensional principal components. Then, with the input of IMFs and principal component, an error correction model based on BiLSTM neural network is established to reduce the inherent error of the model. The experimental results show that the proposed method has higher prediction accuracy than the traditional methods in single-step and multi-step ahead forecasting.