AT-DBN: A Novel Deep Learning Approach for Accurate Flue Gas Pressure Forecasting in Power Plants

Accurately predicting and controlling the flue gas pressure at the induced draft fan inlet is crucial for environmental protection and boiler efficiency. This study designed a model of Attention-enhanced Deep Belief Network (AT-DBN) to predict the flue gas pressure parameters at the induced draft fan inlet, based on key improvements to the Deep Belief Network (DBN). The main improvement involves introducing an attention mechanism to enhance the prediction accuracy of the model for the flue gas pressure parameters at the induced draft fan inlet. The study begins with meticulous preprocessing of historical operational data, including identifying and removing outliers to ensure data quality for subsequent analysis. Next, Pearson correlation analysis is employed to systematically evaluate the correlation between various variables and the flue gas pressure, thereby identifying key variables that significantly affect prediction performance. Finally, an attention mechanism is integrated into the traditional DBN architecture, aiming to enhance the network’s ability to perceive important information within the input features, thereby improving prediction accuracy and model generalization. The results demonstrate that the proposed AT-DBN model can accurately predict the flue gas pressure at the induced draft fan inlet across multiple test sets, providing valuable guidance for power plant production operations.