Explainable Anomaly Detection for District Heating Based on Shapley Additive Explanations

Sungwoo Park, Jihoon Moon, Eenjun Hwang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

One key component in the heat-using facility of district heating systems is the differential pressure control valve. This valve ensures a stable flow of water to the heat exchanger and the temperature control valve. It also makes a stable pressure difference between the supply and return lines. Hence, its malfunctioning could cause significant heat losses and, consequently, economic losses. To avoid this, it is necessary to monitor the abnormal operation of the valve in real-time. Despite various machine learning-based anomaly detection models, their decision is limited in practical use unless the rationale for the decision is appropriately explained. In this paper, we propose a Shapley additive explanation-based explainable anomaly detection scheme that can present the degree of contribution of input variables to the derived result. We report some of the experimental results.

Original languageEnglish
Title of host publicationProceedings - 20th IEEE International Conference on Data Mining Workshops, ICDMW 2020
EditorsGiuseppe Di Fatta, Victor Sheng, Alfredo Cuzzocrea, Carlo Zaniolo, Xindong Wu
PublisherIEEE Computer Society
Pages762-765
Number of pages4
ISBN (Electronic)9781728190129
DOIs
Publication statusPublished - 2020 Nov
Event20th IEEE International Conference on Data Mining Workshops, ICDMW 2020 - Virtual, Sorrento, Italy
Duration: 2020 Nov 172020 Nov 20

Publication series

NameIEEE International Conference on Data Mining Workshops, ICDMW
Volume2020-November
ISSN (Print)2375-9232
ISSN (Electronic)2375-9259

Conference

Conference20th IEEE International Conference on Data Mining Workshops, ICDMW 2020
Country/TerritoryItaly
CityVirtual, Sorrento
Period20/11/1720/11/20

Keywords

  • anomaly detection
  • differential pressure control valve
  • district heating
  • explainable artificial intelligence
  • random forest
  • shapley additive explanations

ASJC Scopus subject areas

  • Computer Science Applications
  • Software

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