Home Journals Articles Updates About
CN
image
收藏切换
Water Consumption Prediction Based on Multi-layer Long and Short Term Memory Neural Network
收藏切换
PDF
Jian WANG, Li LIU, Chun-ying ZHA, Guo-wei CHEN
Water Resources and Power | 2023, 41(12) : 24 - 27
Less
收藏切换
Water Resources and Power | 2023, 41(12): 24-27
• HYDROLOGY, WATER RESOURCES AND ENVIRONMENT •
Water Consumption Prediction Based on Multi-layer Long and Short Term Memory Neural Network
Full
Jian WANG, Li LIU, Chun-ying ZHA, Guo-wei CHEN
Affiliations
  • School of Civil and Hydraulic Engineering, Hefei University of Technology, Hefei 230009, China
Published: 2023-12-25 doi: 10.20040/j.cnki.1000-7709.2023.20230176
Outline
收藏切换

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).

residential water consumption  /  long short-term memory  /  time distributed module  /  multilayer long short-term memory  /  prediction accuracy
Jian WANG, Li LIU, Chun-ying ZHA, Guo-wei CHEN. Water Consumption Prediction Based on Multi-layer Long and Short Term Memory Neural Network[J]. Water Resources and Power, 2023 , 41 (12) : 24 -27 . DOI: 10.20040/j.cnki.1000-7709.2023.20230176
Appendix
Less
Year 2023 volume 41 Issue 12
PDF
73
10
Cite this Article
BibTeX
Article Info
doi: 10.20040/j.cnki.1000-7709.2023.20230176
  • Receive Date：2023-02-12
  • Online Date：2026-01-28
  • Published：2023-12-25
Article Data
Affiliations
History
  • Received：2023-02-12
  • Revised：2023-03-21
Funding
Affiliations
    School of Civil and Hydraulic Engineering, Hefei University of Technology, Hefei 230009, China
References
Share
https://castjournals.cast.org.cn/joweb/sdnykx/EN/10.20040/j.cnki.1000-7709.2023.20230176
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT
Links
Articles
Latest Articles
Most Read
Collections
Updates
Events
News
Multimedia
About
About Us
Contact
No. 86 Xueyuan South Road, Haidian District, Beijing
100081
010-62199257
qkjq@cast.org.cn

Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House