Aiming at the over-temperature problem that is prone to occur in the rear screen superheater piping in the boiler during the peaking and frequency adjustment process of coal power units

this paper proposes an integrated reheater wall temperature prediction model based on Gaussian mixture model (GMM) clustering and attention mechanism. First

the key feature variables are screened by maximum information coefficient (MIC) and correlation-based feature selection (CFS) algorithms Then

the dataset is divided into subsets corresponding to different operational conditions by using the GMM clustering algorithm. Independent prediction models are constructed by using the parallel architectural-temporal convolutional network-long short-term memory (PA-TCN-LSTM). Finally

an integrated prediction model is developed by combining the attention mechanism with the fusion of a single prediction model. Simulation results show that the integrated model he root mean squared error (RMSE) and mean absolute error (MAE) by 20% and 18% compared to single models

with a 66% improvement in generalization ability across different operational conditions. The average absolute error across all conditions is 1.44℃

meeting industrial precision requirements. This scheme effectively improves the adaptability of the prediction model to changes in working conditions

provides technical support for the safe and stable operation of coal power units under complex working conditions

and has important application value.