@inproceedings{fa3d2462a598412f93c81375d2c6496a,
title = "Machine learning for water supply supervision",
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{\textquoteright}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.",
keywords = "Data science, Fault diagnosis, Machine learning",
author = "Thomas Schranz and Gerald Schweiger and Siegfried Pabst and Franz Wotawa",
year = "2020",
month = jan,
day = "1",
doi = "10.1007/978-3-030-55789-8_21",
language = "English",
isbn = "9783030557881",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "238--249",
editor = "Hamido Fujita and Jun Sasaki and Philippe Fournier-Viger and Moonis Ali",
booktitle = "Trends 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",
address = "Germany",
note = "33rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2020 ; Conference date: 22-09-2020 Through 25-09-2020",
}