A fully adaptive forecasting model for short-term drinking water demand

Bakker, M.; Vreeburg, J.H.G.; Schagen, K. M. van; Rietveld, L.C.

doi:10.1016/j.envsoft.2013.06.012

Cited by 98 publications

(71 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…(Wierzbicki et al, 2000): i.e. water resource management and operation (Rani et al, 2010), (Giupponi, 2005), management of complex networks in the urban water cycle (Ocampo et al, 2013), water demand management (Bakker et al, 2013) (Makropoulos et al, 2008), climate change effect on water resources and water supply (Pouget et al, 2012) (Laucelli et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

A decision support system for on-line leakage localization

Meseguer

Mirats-Tur

Cembraño

et al. 2014

Environmental Modelling & Software

View full text Add to dashboard Cite

Abstract:This paper describes a model-driven decision-support system (software tool) implementing a model-based methodology for on-line leakage detection and localization which is useful for a large class of water distribution networks. Since these methods present a certain degree of complexity which limits their use to experts, the proposed software tool focuses on the integration of a method emphasizing its use by water network managers as a decision support system. The proposed software tool integrates a model-based leakage localization methodology based on the use of on-line telemetry information, as well as a water network calibrated hydraulic model. The application of the resulting decision support software tool in a district metered area (DMA) of the Barcelona distribution network is provided and discussed. The obtained results show that the leakage detection and localization may be performed efficiently reducing the required time.

show abstract

Section: Introductionmentioning

confidence: 99%

A decision support system for on-line leakage localization

Meseguer

Mirats-Tur

Cembraño

et al. 2014

Environmental Modelling & Software

View full text Add to dashboard Cite

show abstract

“…Adaptive predictive methods are also found in the literature, e.g., the algorithm proposed by Bakker et al [18], which considers just the last two days for predicting the water demand of the next two days. The contribution of the days is weighted and a complementary fixed calendar is considered as an additional information input.…”

Section: Related Workmentioning

confidence: 99%

Multi-Model Prediction for Demand Forecast in Water Distribution Networks

et al. 2018

View full text Add to dashboard Cite

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.

show abstract

“…The time step used typically ranges from a day [11,18] to an hour [12,19], or to as little as a quarter of an hour in the case of the model of [13]. There are also models with multiperiodicity, in which water demand is forecast at different time steps, for example on a daily and hourly basis, as in [10].…”

Section: Introductionmentioning

confidence: 99%

“…Other models, by contrast, are based primarily on the representation of the periodic patterns that typically characterize demand [13], possibly coupled with techniques of time series analysis, as in [10].…”

Section: Introductionmentioning

confidence: 99%

A Short-Term Water Demand Forecasting Model Using a Moving Window on Previously Observed Data

Pacchin

Alvisi

Franchini

2017

Water

View full text Add to dashboard Cite

Abstract:In this article, a model for forecasting water demands over a 24-h time window using solely a pair of coefficients whose value is updated at every forecasting step is presented. The first coefficient expresses the ratio between the average water demand over the 24 h that follow the time the forecast is made and the average water demand over the 24 h that precede it. The second coefficient expresses the relationship between the average water demand in a generic hour falling within the 24-h forecasting period and the average water demand over that period. These coefficients are estimated using the information available in the weeks prior to the time of forecasting and, therefore, the model does not require any actual calibration process. The length of the time series necessary to implement the model is thus just a few weeks (3-4 weeks) and the model can be parameterized and used without there being any need to collect hourly water demand data for long periods. The application of the model to a real-life case and a comparison with results provided by another model already proposed in the scientific literature show it to be effective, robust, and easy to use.

show abstract

A fully adaptive forecasting model for short-term drinking water demand

Cited by 98 publications

References 28 publications

A decision support system for on-line leakage localization

A decision support system for on-line leakage localization

Multi-Model Prediction for Demand Forecast in Water Distribution Networks

A Short-Term Water Demand Forecasting Model Using a Moving Window on Previously Observed Data

Contact Info

Product

Resources

About