Abstract: To address the issues of noise interference, poor generalization of single models, and difficulty in adapting to complex peak shaving conditions in traditional wall temperature measurement of coal-fired units, a hybrid model for wall temperature prediction based on whale optimization algorithm-variational mode decomposition-convolutional neural network-bidirectional gated recurrent unit(WOA-VMD-CNN-BiGRU) is proposed. This method follows a technical route of data decomposition-feature extraction-time series prediction. firstly, the Pearson correlation coefficient method is used to screen input features strongly related to wall temperature. Then, the whale optimization algorithm(WOA) is employed to optimize the hyperparameters of variational mode decomposition(VMD) to effectively decompose the original wall temperature signal. finally, a CNN-BiGRU hybrid model is constructed, integrating the local feature extraction capability of convolutional neural network(CNN) and the time series modeling ability of bidirectional gated recurrent unit(BiGRU) for prediction. Using actual operation data of power plant units as samples, the performance of the proposed model is compared with that of the BiGRU model in single-step and multi-step prediction scenarios. The results show that in both single-step and multi-step predictions, the prediction error of the proposed model is significantly reduced, and its ability to explain data is significantly improved. This indicates that the proposed method can effectively enhance the accuracy and stability of wall temperature prediction, providing reliable technical support for wall temperature monitoring and over-temperature early warning of coal-fired units.

Key words: wall temperature prediction, whale optimization algorithm, variational mode decomposition, convolutional neural network, bidirectional gated recurrent unit