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Lookup NU author(s): Dr Xiang XieORCiD
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Accurate water demand forecasting can help understand water usage dynamics, which has a potential application in water saving and demand management. Despite extensive research, most exiting methods cannot capture long-range dependency of water demand and maintain high-accuracy in multi-horizon forecasting continuously. To address these issues, the focus of this research is primarily on the temporal model for multi-horizon water demand forecasting using deep learning. This research proposes a signal enhancement strategy and develops an attention-based deep learning model. Firstly, the Fourier transform is used for sparse approximation of water demand data, which helps represent the signal more compactly. To enhance the performance of multi-horizon forecasting model, the complex water demand time series is decomposed into trend and seasonal components. Then, an attention mechanism is utilized to learn temporal dependencies within the data, providing assists for multi-horizon water demand forecasting. Furthermore, a comparative study is carried out between the proposed model and the state-of-the-art methods using the water demand data from four usage scenarios. Experimental results suggest that the present model has the ability to produce accurate and robust forecasting, especially for multi-horizon water demand. Therefore, this research has the potential to enhance water resource management in a socioecological context.
Author(s): Wang K, Xie X, Liu BT, Yu J, Wang ZQ
Publication type: Article
Publication status: Published
Journal: Sustainable Cities and Society
Year: 2024
Volume: 112
Print publication date: 01/10/2024
Online publication date: 13/06/2024
Acceptance date: 11/06/2024
ISSN (print): 2210-6707
ISSN (electronic): 2210-6715
Publisher: Elsevier BV
URL: https://doi.org/10.1016/j.scs.2024.105595
DOI: 10.1016/j.scs.2024.105595
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