Fourier series and Chebyshev polynomials applied to real-time water demand forecasting
Relevance of water demand forecasting increases with complexity of water supply systems. Several methods for water demand forecasting have been proposed in literature, mostly based on time-series analysis and machine learning, the later needing more detailed study of involved variables and choice of...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2017 |
| País: | México |
| Institución: | Universidad de Guanajuato |
| Repositorio: | Repositorio Institucional de la Universidad de Guanajuato |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.ugto.mx:20.500.12059/1759 |
| Acceso en línea: | http://repositorio.ugto.mx/handle/20.500.12059/1759 |
| Access Level: | acceso abierto |
| Palabra clave: | info:eu-repo/classification/cti/1 Water supply networks Real-time forecasting Water demand Fourier series Chebyshev polynomials Redes de abastecimiento de agua Previsión en tiempo real Demanda de agua Series de Fourier Polinomios de Chebyshev |
| Sumario: | Relevance of water demand forecasting increases with complexity of water supply systems. Several methods for water demand forecasting have been proposed in literature, mostly based on time-series analysis and machine learning, the later needing more detailed study of involved variables and choice of the best configuration to produce significant results. As an alternative to use of machine learning methods, this work presents two known meth-ods of data approximation, namely, discrete Fourier series and Chebyshev polynomials, to real-time demand forecasting, through real-time updating of some adjustable coefficients. A real district of a water supply system is analyzed using these methods, showing a good approximation between measured and forecasted values. The most interesting point of ap-plication is agility of calculation and the fact that there is no need to have information on factors influencing water demand |
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