Tools and processes for calibrating large all-pipes models

Jacobsen, Laura B.; Kamojjala, Sri

doi:10.1080/15730620802566869

Cited by 4 publications

(2 citation statements)

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“…Daily model plan. LVVWD uses the hydraulic model to develop a daily operating plan to minimize energy costs and water age (Jacobsen & Kamojjala, 2009). The process consists of a demand‐forecasting component, a reservoir‐cycling component, and pump‐selection tools.…”

Section: Real‐time Modeling Processmentioning

confidence: 99%

Real‐time modeling of water distribution systems: A case study

Boulos¹,

Jacobsen

Heath

et al. 2014

Journal AWWA

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Water utilities worldwide face increasing challenges to preserve the hydraulic and water quality integrity of their water distribution networks. These challenges stem from burgeoning populations and migration to urban cities that continue to increase the load on aging, inefficient, and already strained infrastructures. This has created a pressing need for integrating supervisory control and acquisition systems with network simulation models for proactive management of these networks. Such an integrated platform is the basis for the real‐time smart water network decision support system (SWNDSS) described here. The proposed system has the power to transform a water utility's routine network modeling functions from planning and design to full‐spectrum engagement that drives more efficient operations—including managing water quality and energy, developing daily operating plans, addressing planned and emergency outages, and diagnosing and resolving field issues. Aspects of the SWNDSS ar described in a case study from the Las Vegas Valley Water District in Las Vegas, Nev.

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Section: Real‐time Modeling Processmentioning

confidence: 99%

Real‐time modeling of water distribution systems: A case study

Boulos¹,

Jacobsen

Heath

et al. 2014

Journal AWWA

Self Cite

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“…Pacchin et al () forecasted hourly demands based on simple ratios of recent 24 h total demands and specific time of day demands. Apart from the statistical approaches of MLR and time series analysis, other approaches such as Artificial Neural Networks (ANNs; Bougadis et al, ; Jacobsen & Kamojjala, ; Jain & Ormsbee, ), ensemble ANNs (i.e., generating ANNs for individual hours within a day; Rangel et al, ; Romano & Kapelan, ), wavelet‐bootstrap‐neural networks (Tiwari & Adamowski, ), Relevance Vector Regression with wavelet transforms (Bai et al, ), Support Vector Regression (SVR; Bai et al, ), and SVR with a Fourier series representation of the predicted deviations (Brentan et al, ) have also been used for demand representation and/or forecasting. While these approaches capture nonlinear aspects of demand dynamics, these models have generally been applied to daily, or longer, time intervals for total demands without capturing forecasted uncertainties.…”

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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