Serving as a clean and renewable energy source, wind energy plays a significant role in mitigating the increasingly severe energy crisis. However, the fluctuation and randomness of wind speed pose severe challenges to the stable operation of power systems. To address this issue, a combined short-term wind speed forecasting model named CEEMDAN-RIME-CNN-BiLSTM-AM was proposed, which was based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), rime optimization algorithm(RIME), convolutional neural network (CNN), bidirectional long short-term memory network (BiLSTM), and attention mechanism(AM). Initially, the CEEMDAN algorithm was applied to the original wind speed series to obtain a series of relatively stable submodes, thereby reducing the volatility of the wind speed series. Subsequently, the CNN hyperparameters were optimized using the RIME algorithm to establish the CNN-RIME model for adaptive extraction and mining of wind speed data. Then, the BiLSTM-AM model was employed to forecast the processed data. Finally, the forecasting results of each sub-series were superimposed to obtain the final forecasting result. A comparative experiment was conducted using an actual wind speed dataset from a certain location. The proposed model demonstrates good forecasting performance in both single-step and multi-step forecasting, providing a reference for scheduling plans to maximize energy utilization and power supply.