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Flow meter data validation and reconstruction using neural networks: Application to the Barcelona water network

AutorRodriguez, Hector; Puig, Vicenç; Flores, Juan J.; López, Rodrigo
Fecha de publicación2016
EditorInstitute of Electrical and Electronics Engineers
CitaciónEuropean Control Conference: 16582665 (2016)
ResumenThe use of false or erroneous data can lead to wrong decisions when operating a system. In case of a water distribution network, the use of incorrect data could lead to errors in the billing system, waste of energy, incorrect management of control elements, etc. This paper is focused on detecting flow meters reading abnormalities by exploiting the temporal redundancy of the demand time series by means of artificial neural networks (ANN). Communication problems with the sensor generate missing data and bad maintenance service in the flow meters produce false data. In this work, a methodology to detect the false data (validate) and replace the missing or false data (reconstruct) is proposed. As a core methodology, ANNs are used to model the time series generated from the water demand flow meters, and use the confidence intervals to validate the information. To illustrate the proposed methodology, the application to flow meters in the water distribution network of Barcelona is used.
DescripciónTrabajo presentado a la 15th European Control Conference (ECC) celebrada en Aalborg (Dinamarca) del 29 de junio al 1 de julio de 2016.
Versión del editorhttps://doi.org/10.1109/ECC.2016.7810543
URIhttp://hdl.handle.net/10261/168092
Identificadoresdoi: 10.1109/ECC.2016.7810543
isbn: 978-1-5090-2591-6
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