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Combined Holt-Winters and GA trained ANN approach for sensor validation and reconstruction: application to water demand flowmeters
Rodríguez Rangel, Héctor; Puig Cayuela, Vicenç; Flores, Juan J.; López, Rodrigo
Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial; Universitat Politècnica de Catalunya. SAC - Sistemes Avançats de Control
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This paper proposes a Double Seasonal Holt-Winters (DSHW) forecasting model with an auxiliary Artificial Neural Network (ANN) trained with a Genetic Algorithm (GA) to model the DSHW residuals. ANN complements and improves the DSHW prediction. The proposed model also includes an on-line validation and reconstruction mechanism useful to detect and correct missing and erroneous data. This mechanism also impacts improving the DSHW prediction accuracy and precision. The proposed model and validation mechanism are applied to predict the time series generated by two monitored flowmeters of two sectors of Barcelona's drinking water network (DWN). The accuracy and precision improvement of the proposed method with respect to standard DSHW and ARIMA approaches is provided.
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Àrees temàtiques de la UPC::Informàtica::Sistemes d'informació
Predictive control
Predictive models
Time series analysis
Genetic algorithms
Water resources
Computational modeling
Data models
Training
Control predictiu
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
info:eu-repo/semantics/conferenceObject
IEEE Press
         

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