Machine learning for water supply supervision

Thomas Schranz, Gerald Schweiger, Siegfried Pabst, Franz Wotawa*

*Korrespondierende/r Autor/in für diese Arbeit

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem Konferenzband

Abstract

In an industrial setting water supply systems can be complex. Constructing physical models for fault diagnosis or prediction requires extensive knowledge about the system’s components and characteristics. Through advances in embedded computing, consumption meter data is often readily available. This data can be used to construct black box models that describe system behavior and highlight irregularities such as leakages. In this paper we discuss the application of artificial intelligence to the task of identifying irregular consumption patterns. We describe and evaluate data models based on neural networks and decision trees that were used for consumption prediction in buildings at the Graz University of Technology.

Originalspracheenglisch
TitelTrends in Artificial Intelligence Theory and Applications. Artificial Intelligence Practices - 33rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2020, Proceedings
Redakteure/-innenHamido Fujita, Jun Sasaki, Philippe Fournier-Viger, Moonis Ali
Herausgeber (Verlag)Springer Science and Business Media Deutschland GmbH
Seiten238-249
Seitenumfang12
ISBN (Print)9783030557881
DOIs
PublikationsstatusVeröffentlicht - 1 Jan 2020
Veranstaltung33rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems - Hybrider Event, Japan
Dauer: 22 Sep 202025 Sep 2020

Publikationsreihe

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Band12144 LNAI
ISSN (Print)0302-9743
ISSN (elektronisch)1611-3349

Konferenz

Konferenz33rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems
KurztitelIEA/AIE 2020
LandJapan
OrtHybrider Event
Zeitraum22/09/2025/09/20

ASJC Scopus subject areas

  • !!Theoretical Computer Science
  • !!Computer Science(all)

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