CNN-BiLSTM Short-Term Wind Power Forecasting Method Based on Feature Selection
Yufeng Chen, Hang Zhao, Rui Zhou, Peidong Xu, Kelly Zhang, Yuxin Dai, Haoran Zhang, Jun Zhang, Tianlu Gao
Abstract
Wind power has great uncertainty and short-term wind power forecasting technology can provide great help to power system scheduling after wind power integration. In this paper, a Convolutional neural network -bidirectional long and short-term memory network combination modeliCNN-BiLITMjbased on feature selection is proposed. Firstly, high correlation feature parameters were optimized based on effective feature screening of multidimensional feature datasets. Secondly, the input data are weighted according to the feature correlation to form a multi-dimensional feature data set. Finally, CNN-BiLSTM developed the wind energy forecast model. For verification, the KDD Cup 2022 wind power generation prediction data set was employed. The outcomes demonstrate that CNN-BiLSTM has a greater time series data utilization rate and prediction accuracy.