Forecasting operational demand for an urban water supply zone

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

doi:10.1016/s0022-1694(01)00582-0

Cited by 152 publications

(91 citation statements)

References 14 publications

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“…, Sunday). For each day, there is a particular pattern of hourly consumption [9]. In this section, it is assumed that all public holidays can be treated like Sundays.…”

Section: Estimation Of the Coefficients α T And β Tkmentioning

confidence: 99%

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

Pacchin

Alvisi

Franchini

2017

Water

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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.

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“…, Sunday). For each day, there is a particular pattern of hourly consumption [9]. In this section, it is assumed that all public holidays can be treated like Sundays.…”

Section: Estimation Of the Coefficients α T And β Tkmentioning

confidence: 99%

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

Pacchin

Alvisi

Franchini

2017

Water

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“…Accurate forecasting of short-term water demand can contribute to the efficient operation and management of urban water supply systems, resulting in demand being met efficiently and sustainably CAMPISI-PINTO et al 2012;TIWARI, ADAMOWSKI 2014]. The estimation of future urban water demand is therefore essential to the sustainable planning of regional water-supply systems TIWARI, ADAMOWSKI 2015a, b;ZHOU et al 2002]. Given increases in the diverse components of urban water demand (e.g., residential, public, industrial and commercial use -HANEMANN [1998]), water stress and scarcity have become critical issues [ADAMOWSKI et al 2012a, b, c;DAVIS, KIEFER 2005;GOYAL et al 2014;HAI-DARY et al 2013;KAYAGA, SMOUT 2008].…”

Section: Introductionmentioning

confidence: 99%

“…Short-term urban water de-mand forecasts play a significant role in the optimal operation of pumps, wells, and reservoirs, as well as in informing decisions regarding balanced water resource allocation in the face of urgent water needs [HERRERA et al 2010;JAIN, ORMSBEE 2002;KAME'ENUI 2003]. Urban water is generally allocated according to the experience of operators and average water demand; however, accurate and reliable forecasts of short-term demand can help operators provide water in a more efficient and sustainable manner [ZHOU et al 2002].…”

Section: Introductionmentioning

confidence: 99%

Water demand forecasting using extreme learning machines

Tiwari¹,

Adamowski²,

Adamowski³

2016

Journal of Water and Land Development

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The capacity of recently-developed extreme learning machine (ELM) modelling approaches in forecasting daily urban water demand from limited data, alone or in concert with wavelet analysis (W) or bootstrap (B) methods (i.e., ELM, ELM W , ELM B ), was assessed, and compared to that of equivalent traditional artificial neural network-based models (i.e., ANN, ANN W , ANN B ). The urban water demand forecasting models were developed using 3-year water demand and climate datasets for the city of Calgary, Alberta, Canada. While the hybrid ELM B and ANN B models provided satisfactory 1-day lead-time forecasts of similar accuracy, the ANN W and ELM W models provided greater accuracy, with the ELM W model outperforming the ANN W model. Significant improvement in peak urban water demand prediction was only achieved with the ELM W model. The superiority of the ELM W model over both the ANN W or ANN B models demonstrated the significant role of wavelet transformation in improving the overall performance of the urban water demand model.

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“…As commonly used forecasting techniques, traditional methods such as time series, regression and an autoregressive integrated moving average (ARIMA) as well as soft computing techniques such as fuzzy logic, genetic algorithm, and artificial neural networks are being extensively used for a time-series demand forecasting [6][7][8]. Especially for urban water demand modeling, the ARIMA model has performed more accurately than time-series and multiple regression methods when forecasting demand based on climate variables [9].…”

Section: Introductionmentioning

confidence: 99%

A Framework for Sustainable Urban Water Management through Demand and Supply Forecasting: The Case of Istanbul

et al. 2015

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Abstract:The metropolitan city of Istanbul is becoming overcrowded and the demand for clean water is steeply rising in the city. The use of analytical approaches has become more and more critical for forecasting the water supply and demand balance in the long run. In this research, Istanbul's water supply and demand data is collected for the period during 2006 and 2014. Then, using an autoregressive integrated moving average (ARIMA) model, the time series water supply and demand forecasting model is constructed for the period between 2015 and 2018. Three important sustainability metrics such as water loss to supply ratio, water loss to demand ratio, and water loss to residential demand ratio are also presented. The findings show that residential water demand is responsible for nearly 80% of total water use and the consumption categories including commercial, industrial, agriculture, outdoor, and others have a lower share in total water demand. The results also show that there is a considerable water loss in the water distribution system which requires significant investments on the water supply networks. Furthermore, the forecasting results indicated that pipeline projects will be critical in the near future due to expected increases in the total water demand of Istanbul. The authors suggest that sustainable management of water can be achieved by reducing the residential water use through the use of water efficient OPEN ACCESSSustainability 2015, 7 11051 technologies in households and reduction in water supply loss through investments on distribution infrastructure.

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Forecasting operational demand for an urban water supply zone

Cited by 152 publications

References 14 publications

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

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

Water demand forecasting using extreme learning machines

A Framework for Sustainable Urban Water Management through Demand and Supply Forecasting: The Case of Istanbul

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