为满足高峰时段用气需求和保障管道系统供气可靠，降低下游用户缺气风险，提出了一种基于深度学习的天然气管道调峰优化方法。首先，构建了基于深度学习的管道压力预测模型，旨在捕捉系统边界瞬态变化对节点压力影响；然后，建立了管道系统运行成本最小和管道储气量最大的多目标优化模型，可动态调整储气库采气与压缩机运行方案；最后，以长三角区域天然气管网为例进行方法验证。结果表明：与未优化运行方案相比，所提出的调峰优化方法，可提高天然气管道系统供气可靠度，同时降低管道系统运行成本：优化后管道平均存气量提高至3 362.8万m³，节约运行成本10.6万元。该成果可为天然气管道系统调峰优化提供新的方法借鉴。

To ensure gas supply reliability and reduce the risk of gas shortage for customers, a deep learning-based method for the optimization of peak shaving of natural gas pipeline was proposed. Firstly, a forecasting model of node pressure based on deep learning was developed, aiming to capture the impact of boundary transient changes on the node pressure. Then, a multi-objective optimization model with minimum operation cost and maximum pipeline gas storage capacity was established, which could dynamically optimize the gas storage and gas compressor operating scenarios. Finally, the proposed method was validated on a natural gas pipeline network. The results show that, compared with the operation scenario without optimization, the average gas storage capacity of the pipeline increased to 33.628 million cubic meters after optimization, and the operating cost was saved by RMB 106 000. The results can provide a new insight into the peak shaving of natural gas pipeline systems.