Publication
Combined Holt-Winters and GA trained ANN approach for sensor validation and reconstruction: Application to water demand flowmeters
Conference Article
Conference
Conference on Control and Fault Tolerant Systems (SYSTOL)
Edition
3rd
Pages
202-207
Doc link
http://dx.doi.org/10.1109/SYSTOL.2016.7739751
File
Abstract
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 online 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 respected to standard DSHW and ARIMA approaches is provided.
Categories
control theory.
Scientific reference
H. Rodriguez, V. Puig, J.J. Flores and R. Lopez. Combined Holt-Winters and GA trained ANN approach for sensor validation and reconstruction: Application to water demand flowmeters, 3rd Conference on Control and Fault Tolerant Systems, 2016, Barcelona, pp. 202-207, IEEE.
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