| 摘要: |
| 目前,二次供水泵组运行工况与管网用水需求脱节,泵组长期按最不利工况以恒压变流量模式在低效段运行,导致系统能耗偏高。为改变二次供水的运行模式,提出基于长短期记忆神经网络模型（LSTM）的二次供水系统流量预测方法,实现泵组供水从传统的恒压变流量模式向变压变流量模式的转变。以全国30个小区的流量监测数据为基础,形成二次供水流量特征样本集,提出采用LSTM进行数据挖掘,建立可精准预测二次供水流量的模型,并采用平均绝对误差(EMA)、平均绝对百分比误差(EMAP)、均方根误差(ERMS)预测值与真实值进行评估。评估结果显示,EMA和ERMS均为4.32,EMAP最低仅为0.12%,远低于工程参考的4.94%,具备较高的预测精度与可靠性。从评价指标结果看本研究搭建的LSTM流量预测模型可以应用于二次供水系统流量的预测,为实现二次供水模式从恒压变流量向变压变流量转变提供了创新思路和方法,为推动二次供水节能改造提供可行的实施方案,对推动供水行业的数字化转型具有很好的工程应用价值。 |
| 关键词: 二次供水模式 循环神经网络 长短期记忆神经网络模型 流量预测 节能减碳 |
| DOI：10.11918/202510098 |
| 分类号:TU991 |
| 文献标识码:A |
| 基金项目:“十四五”国家重点研发计划(2024YFC3810900); 中国建筑设计研究院有限公司技术创新项目(1100C080240260) |
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| Flow prediction method for secondary water supply based on LSTM deep learning model |
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ZHAO Li1,WANG Rui1,2
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(1.China Architecture Design & Research Group, Beijing 100044, China; 2.School of Architecture, Tsinghua University, Beijing 100084, China)
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| Abstract: |
| Currently, the operating conditions of the secondary water supply pump set are disconnected from the water demand of the pipeline network. The pump set has operated in the low-efficiency zone for a long time under the constant-pressure and variable-flow mode of the most unfavorable conditions, resulting in high system energy consumption. To change the operation mode of secondary water supply, this paper proposed a flow prediction method for secondary water supply systems based on the long short-term memory (LSTM) neural network model to achieve the transformation of pump set water supply from the traditional constant-pressure and variable-flow mode to the variable-pressure and variable-flow mode. Based on the flow monitoring data of 30 communities in China, this paper formed a characteristic sample set of secondary water supply flow, proposed to use LSTM for data mining to establish a model that can accurately predict the flow of secondary water supply, and used mean absolute error (EMA), mean absolute percentage error (EMAP), and root mean square error (ERMS) to evaluate the predicted values against the true values. The evaluation results show that both EMA and ERMS are 4.32, and the lowest EMAP is only 0.12%, which is far below the engineering reference of 4.94%, demonstrating high prediction accuracy and reliability. From the evaluation results, the LSTM flow prediction model established in this study can be applied to the flow prediction of secondary water supply systems, providing an innovative idea and method for the transformation of secondary water supply from constant-pressure and variable-flow mode to variable-pressure and variable-flow mode, offering a feasible implementation plan for promoting the energy-saving renovation of secondary water supply and having great engineering application value for promoting the digital transformation of the water supply industry. |
| Key words: secondary water supply mode recurrent neural network long short-term memory neural network model flow prediction energy conservation and carbon reduction |
