Abstract:

With the continuous development of the green energy concept，wind power generation has become a research focus due to its renewable and non-polluting characteristics. However，the output of wind turbines exhibits significant volatility and randomness，posing challenges for power dispatch in the grid. To accurately predict wind power and achieve supply-demand balance and stable operation of the power grid，an innovative variational mode decomposition-back-propagation-bidirectional long short-term memory（VMD-BP-BiLSTM） combined model was proposed as a prediction tool. This model used the average values of adjacent data to detect and replaced outliers in the raw data，followed by data normalization to reduce differences and interference between different data sets. After data preprocessing，VMD was applied to decompose historical wind power generation data into multiple modal components with different characteristics. These modal components，along with corresponding meteorological data，were then input into a combined model of BP neural network and BiLSTM model to independently predict each component. Simulation tests of wind power prediction for wind farms in the northwest region showed that，compared to traditional models such as BP neural networks，BiLSTM，extreme learning machine （ELM），and convolutional neural network-long short-term memory（CNN-LSTM） models，the VMD-BP-BiLSTM model demonstrated more accurate prediction ability. The VMD-BP-BiLSTM combined model provides a new approach for wind power prediction.

Key words: wind power prediction, variational mode decomposition, BP neural network, BiLSTM network, combined prediction model