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http://hdl.handle.net/10773/27579
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 |
URI: | http://hdl.handle.net/10773/27579 |
DOI: | 10.1504/IJW.2019.099515 |
ISSN: | 1465-6620 |
Appears in Collections: | TEMA - Artigos DEM - Artigos |
Files in This Item:
File | Description | Size | Format | |
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Coelho and Andrade-Campos 2019 Short-term forecasting (preprint).pdf | 1.13 MB | Adobe PDF | View/Open |
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