ZHANGHao,LIYaochen,YOULan,et al.Predictive Scheduling Strategy for Drainage Pumping Stations Based on Water Level[J].China Water & Wastewater,2026,42(3):122-129.
基于液位的排水泵站预测调度方法研究
- Title:
- Predictive Scheduling Strategy for Drainage Pumping Stations Based on Water Level
- Keywords:
- drainage pumping station; predictive scheduling; water level control; coordinated scheduling
- 摘要:
- 排水泵站的优化调度对于提升排水系统的整体效能至关重要。传统的泵站调度方法存在控制滞后、能耗高、设备损耗大等问题。为此，提出了一种基于液位的排水泵站预测调度方法，通过构建目标液位、在线液位预测模型及联动策略，实现了泵站的协同调度。选取苏州中心城区的3座泵站作为试点，采用深度神经网络构建在线液位预测模型，并结合泵站上下游的实际情况制定联动策略。结果表明，模型的液位预测精度较高，3座泵站的液位控制效果显著提升，液位均方误差分别同比下降31.40%、95.78%和89.70%。此外，泵站的平均液位有所提升，日均电单耗有所下降，实现了节能控制的目标。
- Abstract:
- Optimal scheduling of drainage pumping stations is crucial for improving the overall efficiency of drainage systems. Traditional scheduling methods often suffer from issues such as control delay, high energy consumption, and excessive equipment wear. To address these limitations, this study proposed a predictive scheduling method for drainage pumping stations based on water level. The approach integrated target water level setting, an online water level prediction model, and coordinated control strategies to achieve synergistic operation of pumping stations. Three pumping stations in the central urban area of Suzhou were selected as pilot sites. A deep neural network was applied to develop the online water level prediction model, and coordinated strategies were formulated according to the actual conditions of upstream and downstream pumping stations. The results demonstrated that the model achieved high accuracy in water level prediction. The water level control performance at the three pumping stations improved significantly, with the mean square error decreasing by 31.40%, 95.78%, and 89.70%, respectively. Furthermore, the average water level at the pumping stations increased while the daily power consumption per unit decreased, meeting the objective of energy-efficient control.
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