Cascade Machine Learning Approach Applied to Short-Term Medium Horizon Demand Forecasting-Reference-Cited by-同舟云学术

Cascade Machine Learning Approach Applied to Short-Term Medium Horizon Demand Forecasting

Published:2024-09-03 Issue: Volume:34 Page:42
ISSN:
Container-title:The 3rd International Joint Conference on Water Distribution Systems Analysis & Computing and Control for the Water Industry (WDSA/CCWI 2024)
language:
Short-container-title:

Author:

Brentan Bruno¹^ORCID,Zanfei Ariele²^ORCID,Oberascher Martin³^ORCID,Sitzenfrei Robert³^ORCID,Izquierdo Joaquin⁴^ORCID,Menapace Andrea⁵^ORCID

Affiliation:

1. Hydraulic Engineering and Water Resources Department, School of Engineering, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, Brazil

2. AIAQUA S.r.l., Via Volta 13/A, Bolzano 39100, Italy

3. Department of Infrastructure Engineering, Universität Innsbruck, 6020 Innsbruck, Austria

4. Institute for Multidisciplinary Mathematics, Universitat Politécnica de Valéncia, Valencia 46022, Spain

5. Faculty of Agricultural, Environmental and Food Sciences, Free University of Bozen-Bolzano, Bozen-Bolzano 39100, Italy

Publisher

MDPI

Link

https://www.mdpi.com/2673-4591/69/1/42/pdf

Reference6 articles.

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2. A short-term water demand forecasting model using multivariate long short-term memory with meteorological data;Zanfei;J. Hydroinform.,2022

3. Graph convolutional recurrent neural networks for water demand forecasting;Zanfei;Water Resour. Res.,2022

4. How does missing data imputation affect the forecasting of urban water demand?;Zanfei;J. Water Res. Plan. Manag.,2022

5. Long short-term memory;Hochreiter;Neural Comput.,2018