Predictive models for forecasting hourly urban water demand

Towards sustainable urban water management: A critical reassessment

[1] In this paper, we use a theoretical framework of coupled human and natural systems to review the methodological advances in urban water demand modeling over the past 3 decades. The goal of this review is to quantify the capacity of increasingly complex modeling techniques to account for complex human and natural processes, uncertainty, and resilience across spatial and temporal scales. This review begins with coupled human and natural systems theory and situates urban water demand within this framework. The second section reviews urban water demand literature and summarizes methodological advances in relation to four central themes: (1) interactions within and across multiple spatial and temporal scales, (2) acknowledgment and quantification of uncertainty, (3) identification of thresholds, nonlinear system response, and the consequences for resilience, and (4) the transition from simple statistical modeling to fully integrated dynamic modeling. This review will show that increasingly effective models have resulted from technological advances in spatial science and innovations in statistical methods. These models provide unbiased, accurate estimates of the determinants of urban water demand at increasingly fine spatial and temporal resolution. Dynamic models capable of incorporating alternative future scenarios and local stochastic analysis are leading a trend away from deterministic prediction.

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Urban Water Demand Modeling: Review of Concepts, Methods, and Organizing Principles

This paper reviews the literature on urban water demand forecasting published from 2000 to 2010 to identify methods and models useful for specific water utility decision making problems. Results show that although a wide variety of methods and models have attracted attention, applications of these models differ, depending on the forecast variable, its periodicity and the forecast horizon. Whereas artificial neural networks are more likely to be used for short-term forecasting, econometric models, coupled with simulation or scenario-based forecasting, tend to be used for long-term strategic decisions. Much more attention needs to be given to probabilistic forecasting methods if utilities are to make decisions that reflect the level of uncertainty in future demand forecasts.

Urban Water Demand Forecasting: Review of Methods and Models

Over the last two decades, water smart metering programs have been launched in a number of medium to large cities worldwide to nearly continuously monitor water consumption at the single household level. The availability of data at such very high spatial and temporal resolution advanced the ability in characterizing, modeling, and, ultimately, designing user-oriented residential water demand management strategies. Research to date has been focusing on one or more of these aspects but with limited integration between the specialized methodologies developed so far. This manuscript is the first comprehensive review of the literature in this quickly evolving water research domain. The paper contributes a general framework for the classification of residential water demand modeling studies, which allows revising consolidated approaches, describing emerging trends, and identifying potential future developments. In particular, the future challenges posed by growing population demands, constrained sources of water supply and climate change impacts are expected to require more and more integrated procedures for effectively supporting residential water demand modeling and management in several countries across the world. We review high resolution residential water demand modeling studies.We provide a classification of existing technologies and methodologies.We identify current trends, challenges and opportunities for future development.

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Benefits and challenges of using smart meters for advancing residential water demand modeling and management

Temperature and rainfall thresholds for base use urban water demand modelling

Understanding urban residential end uses of water.

A new daily time series model for East Doncaster, Melbourne, Australia, is being evaluated. The model depends on the postulate that total water use is made up of base use and seasonal use, where base use is characterized by the water use during winter months and seasonal use on seasonal, climatic, and persistence components. Using the daily data collected by Yarra Valley Water for East Doncaster water supply distribution zone and the corresponding rainfall and temperature data from the Bureau of Meteorology from 1990 to 2000, the base values were calculated based on the lowest months of water usage in a year and were correlated with the day of the week and temperature and rainfall. Results revealed these three factors to be statistically significant and therefore, base use to be climate dependent. The seasonal water use is modeled by a series of three equations. The separation of the random component from the climatic variable resulted to a better R2 of 86%. The model is further validated using different ...

Forecasting Residential Water Demand: Case Study

An urban water demand model at a water supply distribution zone level is developed. The model is based on a previous proposition that water use is made up of base use determined from winter consumption and as a function of day of the week, rainfall and temperature and of seasonal use assumed to be composed of the potential use that is dependent on temperature in the absence of rainfall, and of the short effects of climatic factors. Daily data of East Doncaster zone from April 1991 to December 1999 are used in the regression model and is evaluated based on R2 and standard error. The model is tested independently using data collected from 2000-2001.

Peter Roberts

Papers

Temperature and rainfall thresholds for base use urban water demand modelling

Understanding urban residential end uses of water.

Forecasting Residential Water Demand: Case Study

A simple time series approach to modelling urban water demand

Understanding residential water use