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Timely and accurate forecasting residential water consumption is critical to design and operational management of water supply systems. Long short-term memory (LSTM) is an effective data-driven prediction model for water consumption, but it usually relies on a large number of parameter settings. This paper proposed a multilayer long short-term memory neural network model (MLSTM), which was built on the LSTM model by superimposing a time distribution module. The results indicate that the MLSTM model has lower complexity and higher prediction accuracy than the LSTM model, especially for the prediction of peak water consumption with MMAPE reduced by about 60%. Meanwhile, the MLSTM model is insignificantly affected by external environmental conditions (e.g., weather).
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及时准确的居民用水量预测对供水系统的设计和运行管理至关重要。长短期记忆神经网络（LSTM）是一种有效的基于数据驱动的用水量预测模型，但其通常依赖于大量的参数设置。因此，在LSTM模型基础上，通过叠加时间分布模块，提出多层长短期记忆神经网络模型（MLSTM）。与LSTM模型对比分析表明，MLSTM模型具有较低复杂度和更高的预测精度，尤其对于高峰期用水量预测（MMAPE值降低约60%），且受外部环境条件（如天气）的影响较小。
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, authorsList=王健, 刘丽, 查淳膺, 陈国炜)}, authors=[Author(id=1223202685785063713, tenantId=1146029695717560320, journalId=1205116964453384197, articleId=1223202684241559694, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=1220928835@qq.com, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1223202685910892847, tenantId=1146029695717560320, journalId=1205116964453384197, articleId=1223202684241559694, authorId=1223202685785063713, language=EN, stringName=Jian WANG, firstName=Jian, middleName=null, lastName=WANG, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=School of Civil and Hydraulic Engineering, Hefei University of Technology, Hefei 230009, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1223202686007361849, tenantId=1146029695717560320, journalId=1205116964453384197, articleId=1223202684241559694, authorId=1223202685785063713, language=CN, stringName=王健, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=合肥工业大学土木与水利工程学院，安徽 合肥 230009, bio={"content":"
王健（1998-），男，硕士研究生，研究方向为给排水系统优化调度理论与技术，E-mail：1220928835@qq.com
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王健（1998-），男，硕士研究生，研究方向为给排水系统优化调度理论与技术，E-mail：1220928835@qq.com
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