Probability-based Classification Adaptive Optimization CNN-BiLSTM Wind Power Prediction

Wind power forecasting is an effective way to reasonably schedule wind power generation and ensure the stable operation of power systems. However, the impact of physical attribute data related to wind power on forecasting varies, and long-term sequences of original features often contain redundant information and noise, making wind power forecasting a challenging task. To address this, a wind power prediction model based on CNN-BiLSTM (KG-RCBM) is proposed. This model uses Convolutional Neural Network (CNN) for short-term feature extraction to obtain local high-dimensional features, which are then analyzed by Bidirectional Long Short-Term Memory Network (BiLSTM) to capture the long-term trends of these local high-dimensional features and more comprehensive sequence information. This approach effectively reduces inaccuracies caused by the mixing of original data. The attention mechanism is utilized to dynamically allocate weights to the output data, addressing the issue of the model's inability to distinguish the importance of different data. The RIME algorithm is used to optimize the hyperparameters corresponding to each type of prediction model after the Gaussian Mixture Mode (GMM) classification, enabling adaptive wind power forecasting. Finally, Wind power data from Galicia, Spain, 2019, as well as hourly updated climate data from NASA, were utilized to validate our proposed wind power forecasting model. Compared with seven other models, the proposed model's prediction accuracy improves by 2.32% to 4.27%, indicating a higher forecasting precision.