BiLSTM-Based Bidirectional Time Feature Learning for Wind Power Interval Forecasting

Wind power features strong randomness and fluctuation, which greatly hinders stable and safe grid system operation. Interval prediction can effectively characterize the variation bounds of wind power generation, thus serving a critical function in the grid-integrated scheduling of wind farms. However, existing methods often struggle to balance interval coverage and interval width, and the bidirectional temporal dependencies in wind power series are not fully exploited. To solve these problems, this research constructs a wind power interval prediction model based on bidirectional long short-term memory (BiLSTM). Using actual operational data from a North China wind farm, the Pearson correlation coefficient is employed to select input features, while Min-Max normalization is utilized to remove dimensional differences among various data types. The model extracts bidirectional temporal features by integrating forward and backward sequence information, and constructs prediction intervals by combining point forecasts with an error superposition method. At a 90% confidence level, performance comparisons are performed between our model and typical recurrent networks including RNN, GRU, LSTM and BiGRU. The findings demonstrate that the BiLSTM model obtains an MAE of 2.091, a PICP of 91.89% and a PINAW of 0.135, which realizes an excellent trade-off between interval coverage and compactness. The proposed method is able to effectively balance point forecasting accuracy and interval prediction quality, and can provide useful support for wind power dispatch and operation.