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• W3000454981 startingPage "104633" @default.
• W3000454981 abstract "Smart meters installed at the user-level provide a new data source for managing water infrastructure. This research explores the use of machine learning methods, including Random Forests (RFs), Artificial Neural Networks (ANNs), and Support Vector Regression (SVR) to forecast hourly water demand at 90 accounts using smart-metered data. Demands are predicted using lagged demand, seasonality, weather, and household characteristics. Time-series clustering is applied to delineate data based on the time of day and day of the week, which improves model performance. Two modeling approaches are compared. Individual models are developed separately for each meter, and a Group model is trained using a data set of multiple meters. Individual models predict demands at meters in the original data set with lower error than Group models, while the Group model predicts demands at new meters with lower error than Individual models. Results demonstrate that RF and ANN perform better than SVR across all scenarios." @default.
• W3000454981 created "2020-01-23" @default.
• W3000454981 creator A5007318256 @default.
• W3000454981 creator A5016610098 @default.
• W3000454981 creator A5081770291 @default.
• W3000454981 date "2020-03-01" @default.
• W3000454981 modified "2023-10-14" @default.
• W3000454981 title "Smart meters data for modeling and forecasting water demand at the user-level" @default.
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• W3000454981 doi "https://doi.org/10.1016/j.envsoft.2020.104633" @default.
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