在可再生能源大规模并网背景下，火电机组的调峰运行大幅增加了锅炉NO_x排放控制的难度。锅炉NO_x排放的实时预测，对于指导火电机组调峰工况下的高效、清洁运行具有重要意义。CFD计算方法涉及多个物理场的耦合迭代计算，计算量巨大，难以实时地建立锅炉运行参数与NO_x排放质量浓度之间复杂的非线性映射关系。随着大数据分析和人工智能技术的快速发展，数据驱动建模为锅炉NO_x的实时预测与控制提供了新方法。锅炉调峰运行状态下的运行数据具有明显的时序特征，同时亦存在着复杂的空间关联特征。然而，目前所普遍采用的深度神经网络等方法无法有效地识别运行数据的时−空关联特征，因此限制了其准确预测NO_x排放的能力。针对上述问题，提出一种基于时空注意力图卷积网络(AST-GCN)的锅炉NO_x预测模型。该模型不仅可以在空间维度挖掘运行参数间的关联特征，还能捕捉历史运行数据与NO_x排放质量浓度的动态映射关系。此外，模型中所嵌入的注意力机制能够进一步提高对运行数据中时空相关特征的动态提取能力，并增加了模型的可解释性，从而可用于指导锅炉在调峰运行下对关键运行参数的优化调整。基于某600 MW锅炉实际运行数据的预测结果表明，相较于传统神经网络模型，AST-GCN模型由于可有效提取锅炉运行参数间的空间关联与时序动态特性，显著提升了模型的预测精度和泛化性能。

Due to the introduction of large-scale renewable energy to the electric grid, the coal-fired units are running more under load cycling conditions and this has dramatically increased the difficulty of boiler in the control of NO_x emissions. The real-time prediction of NO_x emissions is of great significance to guide the efficient and clean operation of thermal power units under load cycling conditions. The CFD methods require huge computational cost to solve multiple coupled conservation equations, and hence, are infeasible to establish the complex nonlinear mapping relationship between boiler operating parameters and NO_x emissions in a real-time manner. With the rapid development of big data analysis and artificial intelligence, data-driven models provide a new approach to realize the real-time prediction and control of boiler NO_x emissions. Under load cycling conditions the boiler operating data has strong correlation in time sequence and complex spatial correlation characteristics. However, the traditional neural network methods cannot effectively capture these correlation characteristics, thus, greatly limiting their ability in the accurate prediction of boiler NO_x emission. To resolve this problem, a boiler NO_x prediction model based on the spatiotemporal attention graph convolutional network (AST-GCN) is proposed in this study. Not only can this model capture the spatial correlations among the boiler operating parameters, but also can it capture the dynamic relationship between the historical boiler operating data and NO_x emissions. In addition, the attention mechanism embedded in the model can further improve the ability to adaptively extract the spatio-temporal correlation features from the boiler operating data, and increase the interpretability of the model. The prediction results based on the operating data of a 600 MW boiler demonstrate that the AST-GCN model exhibits a greatly improved prediction accuracy and generalization performance since it can effectively capture the spatial and time sequence correlations among the boiler operating parameters in comparison to the traditional neural network models.