An enhanced feature extraction based long short-term memory neural network for wind power forecasting via considering the missing data reconstruction

Wind power forecasting plays a significant role in regulating the peak and frequency of the power system, which can improve the wind power receiving capacity. Despite plenty of forecasting methods have been proposed to fortify the accuracy of forecasting, the existing forecasting models do not consider the reconstruction of missing data and can not extract the spatiotemporal features from the wind power data. To address these issues, this study proposes an improved long short-term memory (LSTM) network based forecasting method to reconstruct the missing data and capture the spatiotemporal features from wind power data. In order to fortify the wind power forecasting accuracy of the model, the multiple imputation technique (MIT) is first developed to fill up the missing samples with reconstructed data samples by analyzing the correlation among variables in the raw wind power data. Secondly, to exploit the spatial and temporal features and reduce the low computation complexity, a new parallel convolutional network involving dilated convolution and causal convolution is established for features extraction. Finally, to further improve the wind power forecasting performance, the LSTM is applied to extract the long-term trends and reveal the internal relations of the derived spatiotemporal features. The experimental results on a benchmark dataset and an experimental dataset both demonstrate that the proposed LSTM based wind power forecasting can obtain the better forecasting performance.