Forecasting daily urban water demand: a case study of Melbourne

Zhou, Senlin; McMahon, Thomas A.; Walton, Andrea; Lewis, Jeffrey

doi:10.1016/s0022-1694(00)00287-0

Cited by 213 publications

(149 citation statements)

References 2 publications

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“…Donkor et al [10] report the use of different methodologies to improve the water demand prediction in the short term (from several hours to several days ahead) and the long term (one year or more ahead); nevertheless, it does not propose the use of regimes. Other statistical and machine learning modeling methodologies that deal with the water demand forecasting problem are found in [7,[11][12][13][14]. The work of Cutore et al [15] implement similar ideas about regimes associated with working and holy days to predict one day ahead water demand of daily time series using an ANN.…”

Section: Related Workmentioning

confidence: 99%

Multi-Model Prediction for Demand Forecast in Water Distribution Networks

et al. 2018

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This paper presents a multi-model predictor called Qualitative Multi-Model Predictor Plus (QMMP+) for demand forecast in water distribution networks. QMMP+ is based on the decomposition of the quantitative and qualitative information of the time-series. The quantitative component (i.e., the daily consumption prediction) is forecasted and the pattern mode estimated using a Nearest Neighbor (NN) classifier and a Calendar. The patterns are updated via a simple Moving Average scheme. The NN classifier and the Calendar are executed simultaneously every period and the most suited model for prediction is selected using a probabilistic approach. The proposed solution for water demand forecast is compared against Radial Basis Function Artificial Neural Networks (RBF-ANN), the statistical Autoregressive Integrated Moving Average (ARIMA), and Double Seasonal Holt-Winters (DSHW) approaches, providing the best results when applied to real demand of the Barcelona Water Distribution Network. QMMP+ has demonstrated that the special modelling treatment of water consumption patterns improves the forecasting accuracy.

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Section: Related Workmentioning

confidence: 99%

Multi-Model Prediction for Demand Forecast in Water Distribution Networks

et al. 2018

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“…However, too many factors should be considered in short predicted, which may affect the precision of prediction. So, the regression analysis model is used to analyze historical data by factors of influence, which employs quantitative management equation to demonstrate the relationship of factors and water demands [2]. Thus, Building the regression equation can improve effectiveness of the short predicion.…”

Section: Introductionmentioning

confidence: 99%

Study of PSO-SVM Model for Daily Water Demand Prediction

Jiang¹,

Mu²,

Zhao³

et al. 2017

Proceedings of the 2017 5th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering (ICMMCCE 20

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Abstract:For solving the problem of daily water demand in G city, a method of particle swarm optimization algorithm combined support vector machine (PSO-SVM) is presented. The expansion constant and penalty factor firstly are selected by particle swarm optimization (PSO). Secondly, the historical water demands data are trained by support vector machine (SVM). Finally, the new independent variables are employed to predict water demands in next time. By comparing BP and SVM with this method, the results show that the real and predicted daily water demands errors are less than the other models. Therefore, this method is a effective way to predict daily water demands of city G.

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“…León et al (2000), motivados pelas necessidades de redução de custos com energia elétrica, de otimização operacional e de ações de gestão de demanda da água no SAA da cidade de Seville, na Espanha, desenvolveram um SH denominado EXPLORE, os resultados indicam redução de 25% nos custos com energia e benefícios adicionais como qualificação dos operadores menos experientes. Para realizar, eficientemente, a distribuição diária de água, Zhou et al (2000) formularam um modelo matemático computacional capaz de prever o consumo de água para o próximo dia, os resultados indicaram um modelo com performance satisfatória. O modelo proposto por Zhou et al (2000) se fundamentou na hipótese de que a série temporal de consumo é igual ao somatório do consumo de base e o consumo sazonal (componentes sazonal, climático e de persistência).…”

Section: Introductionunclassified

“…Para realizar, eficientemente, a distribuição diária de água, Zhou et al (2000) formularam um modelo matemático computacional capaz de prever o consumo de água para o próximo dia, os resultados indicaram um modelo com performance satisfatória. O modelo proposto por Zhou et al (2000) se fundamentou na hipótese de que a série temporal de consumo é igual ao somatório do consumo de base e o consumo sazonal (componentes sazonal, climático e de persistência).…”

Section: Introductionunclassified

Previsão consumo diário de água: um estudo de caso de Nobres (Brasil)

2016

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Para obter eficiência operacional em sistema de abastecimento de água (SAA) a previsão de consumo de água em curto prazo (para o próximo dia) é necessária. Esse trabalho teve o objetivo de desenvolver um modelo matemático de previsão do consumo diário de água da cidade de Nobres, Estado de Mato Grosso, no Brasil. As etapas metodológicas realizadas foram: (1) revisão de literatura; (2) coleta e análise de dados (consumo de água e clima); (3) proposição de modelo de previsão de consumo diário de água; (4) calibração e verificação do modelo; e, (5) aplicação de modelo. As técnicas de modelagem matemática empregadas foram Regressão Linear, Séries de Fourier e Sistema Especialista. Os resultados indicaram um erro médio percentual do modelo inferior a 10% indicando que apresentou bom ajuste e que pode ser utilizado para prever o consumo de água. Como principal conclusão apresenta-se que o modelo desenvolvido pode ser utilizado para o planejamento operacional do SAA estudado.Palavras chave | Demanda de água; Regressão Linear; Séries de Fourier; Sistema Especialista. ABSTRACT In order to move operational efficiency from the water system supply (Waterworks, i.e. a combined water purification plant and pumping station) forecast water consumption values 24 h ahead are required. The objective of this paper is to develop mathematical model forecast water consumption 24 h ahead for Nobres city, Mato Grosso

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Forecasting daily urban water demand: a case study of Melbourne

Cited by 213 publications

References 2 publications

Multi-Model Prediction for Demand Forecast in Water Distribution Networks

Multi-Model Prediction for Demand Forecast in Water Distribution Networks

Study of PSO-SVM Model for Daily Water Demand Prediction

Previsão consumo diário de água: um estudo de caso de Nobres (Brasil)

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