Abstract

This research utilized a series of historical wind speed observations and corresponding wind power generation data as training data. Based on the SSA-CNN-BiLSTM neural network model, the paper first preprocessed the historical data to extract relevant features related to wind power generation. Subsequently, a deep learning model based on the integration of Sparrow Search Algorithm, Convolutional Neural Network, and Bidirectional Long Short-Term Memory Network was constructed. This model leveraged the global search optimization capability of SSA and the advantages of CNN-BiLSTM in sequence and image processing. Through experimentation, predictive results were obtained and compared with real data to validate the rationality and accuracy of the algorithm. The experimental outcomes demonstrated a significant improvement in wind power generation prediction using the SSA-CNN-BiLSTM model. This confirmed the superiority of the model in addressing wind power generation prediction tasks and provided valuable references and applications for enhancing the reliability and efficiency of the wind power industry.

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