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Title: Short-term forecasting of hourly water demands: a Portuguese case study
Author: Coelho, Bernardete
Andrade-Campos, António
Keywords: Water demand forecasting
Artificial Neural Networks
Data analysis
Exponential Smoothing
Naïve methods
Portuguese water network
Issue Date: 2-May-2019
Publisher: Inderscience
Abstract: Predicting future water demands is becoming essential for the efficient management of water supply systems (WSS). To improve the operations of a Portuguese network, short-term water demand forecasting models are applied to a number of datasets collected from distinct locations in the network. Traditional forecasting models, such as exponential smoothing and naïve models, and artificial neural network (ANN)-based models are developed and compared. Additionally, the influence of anthropic and weather variables in the ANN-based models is also analysed. Results demonstrate that, for this case-study, ANN-based models outperform the traditional models when external predictors such as anthropic and weather variables are included in the models. However, the inappropriate choice of such variables may lead to worse forecasting performances.
Peer review: yes
DOI: 10.1504/IJW.2019.099515
ISSN: 1465-6620
Appears in Collections:TEMA - Artigos
DEM - Artigos

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