机器学习驱动下给水管网需水精准预测与全流程低碳调度技术

Machine Learning-Driven Precise Water Demand Forecasting and Full-Process Low-Carbon Scheduling Technology for Water Supply Networks

摘要: 城市给水管网系统作为城市重要基础设施，其运行过程中的能耗与碳排放问题日益受到关注。在“双碳”战略目标背景下，如何实现给水管网的低碳化运行已成为水务工程领域的研究热点。研究从全生命周期视角出发，系统梳理了给水管网碳排放来源与构成，重点综述了机器学习技术在需水预测与低碳调度领域的应用进展。研究表明，给水管网运行阶段碳排放占全生命周期排放总量的80%以上，其中泵站能耗是主要排放源。长短期记忆网络(LSTM)、图神经网络(GNN)、随机森林(RF)及XGBoost等机器学习模型在需水预测中展现出显著优势，预测精度可达90%以上。深度强化学习与多目标优化算法在泵站调度优化中的应用可实现10%~30%的节能效果。结合国内外的典型城市案例，进一步探讨了机器学习驱动的给水管网低碳运行技术路径与未来发展方向。

Abstract: As a vital urban infrastructure, the energy consumption and carbon emissions of urban water supply network systems are receiving increasing attention. Against the background of the “dual carbon” strategic goals, achieving low-carbon operation of water supply networks has become a research hotspot in the field of water engineering. This study systematically reviews the sources and composition of carbon emissions in water supply networks from a full life cycle perspective, focusing on the application progress of machine learning technology in water demand forecasting and low-carbon scheduling. The study shows that carbon emissions during the operation phase of water supply networks account for more than 80% of the total life cycle emissions, with pump station energy consumption being the main source of emissions. Machine learning models such as Long Short-Term Memory (LSTM) networks, Graph Neural Networks (GNN), Random Forest (RF), and XGBoost have shown significant advantages in water demand forecasting, with prediction accuracy exceeding 90%. The application of deep reinforcement learning and multi-objective optimization algorithms in pump station scheduling optimization can achieve energy savings of 10% to 30%. Based on typical urban cases at home and abroad, this study further explores the technical pathways and future development directions of machine learning-driven low-carbon operation of water supply networks.