Tailoring Seasonal Time Series Models to Forecast Short-Term Water Demand

Arandia, Ernesto; Bâ, Amadou; Eck, Bradley; McKenna, Sean Andrew

doi:10.1061/(asce)wr.1943-5452.0000591

Cited by 50 publications

(23 citation statements)

References 46 publications

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“…Sub-hourly, hourly, and daily water predictions were provided by the approach presented in [25]. In particular, the approach was based on coupled Seasonal ARIMA (SARIMA) models with data assimilation that were tailored to be applied to three different datasets (all of them with different quality characteristics).…”

Section: Related Workmentioning

confidence: 99%

A Short-Term Data Based Water Consumption Prediction Approach

et al. 2019

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A smart water network consists of a large number of devices that measure a wide range of parameters present in distribution networks in an automatic and continuous way. Among these data, you can find the flow, pressure, or totalizer measurements that, when processed with appropriate algorithms, allow for leakage detection at an early stage. These algorithms are mainly based on water demand forecasting. Different approaches for the prediction of water demand are available in the literature. Although they present successful results at different levels, they have two main drawbacks: the inclusion of several seasonalities is quite cumbersome, and the fitting horizons are not very large. With the aim of solving these problems, we present the application of pattern similarity-based techniques to the water demand forecasting problem. The use of these techniques removes the need to determine the annual seasonality and, at the same time, extends the horizon of prediction to 24 h. The algorithm has been tested in the context of a real project for the detection and location of leaks at an early stage by means of demand forecasting, and good results were obtained, which are also presented in this paper.

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Section: Related Workmentioning

confidence: 99%

A Short-Term Data Based Water Consumption Prediction Approach

et al. 2019

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“…For descriptive purposes, the parameter estimation only needs to be conducted once. However, for long‐term forecasting purposes, the parameters may be prone to changes (e.g., Arandia‐Perez et al () demonstrated that updating the parameters of a single‐seasonal ARIMA at fixed time intervals improved performance), and an efficient change detection method is useful in determining the correct timing for parameter reestimation. The change detection problem in correlated time series data were studied by Basseville and Nikiforov () and Lai () in which the Generalized Likelihood Ratio (GLR) algorithm was proposed.…”

Section: Model Identification Parameter Estimation and Forecastingmentioning

confidence: 99%

“…Arandia‐Perez () evaluated aggregated demands collected from a regional area within Cincinnati, OH, and demonstrated that single‐seasonal ARIMA models (including either 24 h or weekly periodicities) could describe hourly regional water demands. More recently, Arandia‐Perez et al () compared the performance of single‐seasonal ARIMA models on total system and regional aggregated demands using 15 min, 1 h, and 24 h demand data from Dublin, Ireland, which included an extended Kalman filtering updating process at fixed time intervals. These results demonstrated that the performance of the seasonal ARIMA models improved with larger spatial aggregation and reestimation of the model parameters.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Hourly Water Demands With Seasonal Autoregressive Models for Real‐Time Application

Jin-duan

Boccelli

2018

Water Resources Research

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Consumer water demands are not typically measured at temporal or spatial scales adequate to support real‐time decision making, and recent approaches for estimating unobserved demands using observed hydraulic measurements are generally not capable of forecasting demands and uncertainty information. While time series modeling has shown promise for representing total system demands, these models have generally not been evaluated at spatial scales appropriate for representative real‐time modeling. This study investigates the use of a double‐seasonal time series model to capture daily and weekly autocorrelations to both total system demands and regional aggregated demands at a scale that would capture demand variability across a distribution system. Emphasis was placed on the ability to forecast demands and quantify uncertainties with results compared to traditional time series pattern‐based demand models as well as nonseasonal and single‐seasonal time series models. Additional research included the implementation of an adaptive‐parameter estimation scheme to update the time series model when unobserved changes occurred in the system. For two case studies, results showed that (1) for the smaller‐scale aggregated water demands, the log‐transformed time series model resulted in improved forecasts, (2) the double‐seasonal model outperformed other models in terms of forecasting errors, and (3) the adaptive adjustment of parameters during forecasting improved the accuracy of the generated prediction intervals. These results illustrate the capabilities of time series modeling to forecast both water demands and uncertainty estimates at spatial scales commensurate for real‐time modeling applications and provide a foundation for developing a real‐time integrated demand‐hydraulic model.

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“…Arandia et al [33] proposed a methodology to predict 15 min, hourly, and daily water demand either offline (using historical data) or online (using a real-time feed of data). eir proposal joined seasonal ARIMA (SARIMA) and data assimilation.…”

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confidence: 99%

A High-Frequency Data-Driven Machine Learning Approach for Demand Forecasting in Smart Cities

Preciado

Prieto

Benítez

et al. 2019

Scientific Programming

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Different types of sensors along the distribution pipelines are continuously measuring different parameters in Smart WAter Networks (SWAN). The huge amount of data generated contain measurements such as flow or pressure. Applying suitable algorithms to these data can warn about the possibility of leakage within the distribution network as soon as the data are gathered. Currently, the algorithms that deal with this problem are the result of numerous short-term water demand forecasting (WDF) approaches. However, in general, these WDF approaches share two shortcomings. The first one is that they provide low-frequency predictions. That is, most of them only provide predictions with 1-hour time steps, and only a few provide predictions with 15 min time steps. The second one is that most of them require estimating the annual seasonality or taking into account not only data about water demand but also about other factors, such as weather data, that make their use more complicated. To overcome these weaknesses, this work presents an approach to forecast the water demand based on pattern recognition and pattern-similarity techniques. The approach has a twofold contribution. Firstly, the predictions are provided with 1 min time steps within a time lead of 24 hours. Secondly, the laborious estimation of annual seasonality or the addition of other factors, such as weather data, is not needed. The paper also presents the promising results obtained after applying the approach for water demand forecasting to a real project for the detection and location of water leakages.

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Tailoring Seasonal Time Series Models to Forecast Short-Term Water Demand

Cited by 50 publications

References 46 publications

A Short-Term Data Based Water Consumption Prediction Approach

A Short-Term Data Based Water Consumption Prediction Approach

Forecasting Hourly Water Demands With Seasonal Autoregressive Models for Real‐Time Application

A High-Frequency Data-Driven Machine Learning Approach for Demand Forecasting in Smart Cities

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