Geranmehr, M. orcid.org/0000-0001-7075-915X, Seyoum, A.G. orcid.org/0000-0003-0848-4911 and Heris, M.K. (2024) Battle of water demand forecasting: an optimized deep learning model. In: Alvisi, S., Franchini, M., Marsili, V. and Mazzoni, F., (eds.) Proceedings of The 3rd International Joint Conference on Water Distribution Systems Analysis & Computing and Control for the Water Industry (WDSA/CCWI 2024). 3rd International Joint Conference on Water Distribution Systems Analysis & Computing and Control for the Water Industry (WDSA/CCWI 2024), 01-04 Jul 2024, Ferrara, Italy. Engineering Proceedings, 69 (1). MDPI
Abstract
Ensuring a steady supply of drinking water is crucial for communities, but predicting how much water will be needed is challenging because of uncertainties. As a part of Battle of Water Demand Forecasting (BWDF), this study delves into the application of Long Short-Term Memory (LSTM) networks for water demand forecasting in a city situated in the northeast of Italy. The focus is on forecasting the demand across ten distinct District Metering Areas (DMAs) over four distinct stages. To enhance the performance of the LSTM model, an evolutionary optimization algorithm is integrated, aiming to fine-tune the model’s hyper-parameters effectively. Results indicate the promising potential of this approach for short-term demand forecasting.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
Keywords: | deep learning; demand forecasting; optimization; water distribution network |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > School of Mechanical, Aerospace and Civil Engineering |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 14 Oct 2024 15:20 |
Last Modified: | 14 Oct 2024 15:20 |
Status: | Published |
Publisher: | MDPI |
Series Name: | Engineering Proceedings |
Refereed: | Yes |
Identification Number: | 10.3390/engproc2024069056 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:218373 |