Short-term wind power prediction for wind farm clusters based on SFFS feature selection and BLSTM deep learning

Abstract

Wind power prediction (WPP) of wind farm clusters is important to the safe operation and economic dispatch of the power system, but it faces two challenges: (1) The dimensions of the input parameters for WPP of wind farm clusters are very high so that the input parameters contain irrelevant or redundant features; (2) it is difficult to build a holistic WPP model with high-dimensional input parameters for wind farm clusters. To overcome these challenges, a novel short-term WPP model for wind farm clusters, based on sequential floating forward selection (SFFS) feature selection and bidirectional long short-term memory (BLSTM) deep learning, is proposed in this paper. First, more than 300,000 input features of the wind farm cluster are constructed. Second, the SFFS method is applied to sort the high-dimensional features and analyze the rule that the forecasting accuracy changes with the number of features to obtain the optimal number of features and feature sets. Finally, based on the results of feature selection, BLSTM is applied to build a WPP model for wind farm clusters with a combination of feature selection and deep learning. This case study shows that (1) SFFS is an effective method for selecting the core features for WPP of wind farm clusters; (2) BLSTM shows not only higher WPP accuracy than long short-term memory and backpropagation neural network but also outstanding performance in terms of reducing the phase errors of WPP.