Abstract: In order to reduce the randomness and volatility of wind speed and further improve the prediction accuracy, a combined prediction model combining with quadratic mode decomposition, which can improve dung beetle optimizer and bidirectional gated recurrent unit was proposed. Firstly, aiming at the problems such as local optimization and poor global search ability in Dung Beetle optimizer (DBO), the improved algorithm (LDBO) is improved by introducing Latin hypercube sampling, tangential flight and other strategies, and the improved algorithm (LDBO) is used for parameter optimization of BiGRU. Secondly, the quadratic modal decomposition is used to reduce the complexity of the original data and provide stable sequence data for subsequent modeling. Then, BiGRU is used to predict each modal component obtained after the quadratic mode decomposition, and the prediction results of each modal component are superimposed as the final prediction results. Finally, the proposed QMD-LDBO-BiGRU prediction model is compared with other four mainstream prediction models (CNN-LSTM, TCN-RVM, ELM-Adaboost, BiTCN-SVM). Experimental results show that the evaluation index R² of the QMD-LDBO-BiGRU model reaches 98.086%, which is increased by 21.396, 19.525, 11.474, 5.457 percentage points compared with the comparison model, respectively, which verifies the effectiveness and applicability of the proposed model and provides a certain reference for further improving the accuracy of wind speed prediction.

Key words: wind speed prediction, quadratic mode decomposition, CEEMDAN, VMD, Dung beetle optimizer, bidirectional gated recurrent unit