An Advanced Learning-Based Multiple Model Control Supervisor for Pumping Stations in a Smart Water Distribution System

Alexandru Predescu, Ciprian-Octavian Truică, Elena-Simona Apostol, Mariana Mocanu and Ciprian Lupu
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Alexandru Predescu: Computer Science and Engineering Department, Faculty of Automatic Control and Computers, University POLITEHNICA of Bucharest, Splaiul Independenței 313, 060042 Bucharest, Romania
Ciprian-Octavian Truică: Computer Science and Engineering Department, Faculty of Automatic Control and Computers, University POLITEHNICA of Bucharest, Splaiul Independenței 313, 060042 Bucharest, Romania
Elena-Simona Apostol: Computer Science and Engineering Department, Faculty of Automatic Control and Computers, University POLITEHNICA of Bucharest, Splaiul Independenței 313, 060042 Bucharest, Romania
Mariana Mocanu: Computer Science and Engineering Department, Faculty of Automatic Control and Computers, University POLITEHNICA of Bucharest, Splaiul Independenței 313, 060042 Bucharest, Romania
Ciprian Lupu: Department of Automatic Control and Systems Engineering, University POLITEHNICA of Bucharest, Splaiul Independenței 313, 060042 Bucharest, Romania

Mathematics, 2020, vol. 8, issue 6, 1-29

Abstract: Water distribution is fundamental to modern society, and there are many associated challenges in the context of large metropolitan areas. A multi-domain approach is required for designing modern solutions for the existing infrastructure, including control and monitoring systems, data science and Machine Learning. Considering the large scale water distribution networks in metropolitan areas, machine and deep learning algorithms can provide improved adaptability for control applications. This paper presents a monitoring and control machine learning-based architecture for a smart water distribution system. Automated test scenarios and learning methods are proposed and designed to predict the network configuration for a modern implementation of a multiple model control supervisor with increased adaptability to changing operating conditions. The high-level processing and components for smart water distribution systems are supported by the smart meters, providing real-time data, push-based and decoupled software architectures and reactive programming.

Keywords: smart water networks; internet of things; deep learning; machine learning; multiple model control supervisor (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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