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- W2365332139 abstract "As traditional least squares support vector machine(LSSVM) parameter selection using cross-validation is time-consuming,a city hourly water consumption forecasting method based on genetic algorithm(GA) and LSSVM was proposed.An autocorrelation method was used to analyze the hourly water consumption series according to the strong serial correlation.A self-adaptive binary GA was introduced to optimize the hyper-parameters of LSSVM,and the individual fitness values in GA were determined by cross-validation.Then a hourly water consumption forecasting model was built.Case study shows that the proposed hourly water consumption forecasting method based on GA and LSSVM has higher computing speed and better estimating performance than the traditional LSSVM–based method." @default.
- W2365332139 created "2016-06-24" @default.
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- W2365332139 date "2011-07-14" @default.
- W2365332139 modified "2023-09-24" @default.
- W2365332139 title "Genetic least squares support vector machine approach to hourly water consumption prediction" @default.
- W2365332139 doi "https://doi.org/10.3785/j.issn.1008-973x.2011.06.023" @default.
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