Explainable Anomaly Detection for District Heating Based on Shapley Additive Explanations

Sungwoo Park; Jihoon Moon; Eenjun Hwang

doi:10.1109/ICDMW51313.2020.00111

Explainable Anomaly Detection for District Heating Based on Shapley Additive Explanations

Sungwoo Park, Jihoon Moon, Eenjun Hwang

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

26 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 language	English
Title of host publication	Proceedings - 20th IEEE International Conference on Data Mining Workshops, ICDMW 2020
Editors	Giuseppe Di Fatta, Victor Sheng, Alfredo Cuzzocrea, Carlo Zaniolo, Xindong Wu
Publisher	IEEE Computer Society
Pages	762-765
Number of pages	4
ISBN (Electronic)	9781728190129
DOIs	https://doi.org/10.1109/ICDMW51313.2020.00111
Publication status	Published - 2020 Nov
Event	20th IEEE International Conference on Data Mining Workshops, ICDMW 2020 - Virtual, Sorrento, Italy Duration: 2020 Nov 17 → 2020 Nov 20

Publication series

Name	IEEE International Conference on Data Mining Workshops, ICDMW
Volume	2020-November
ISSN (Print)	2375-9232
ISSN (Electronic)	2375-9259

Conference

Conference	20th IEEE International Conference on Data Mining Workshops, ICDMW 2020
Country/Territory	Italy
City	Virtual, Sorrento
Period	20/11/17 → 20/11/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/ICDMW51313.2020.00111

Cite this

Park, S., Moon, J., & Hwang, E. (2020). Explainable Anomaly Detection for District Heating Based on Shapley Additive Explanations. In G. Di Fatta, V. Sheng, A. Cuzzocrea, C. Zaniolo, & X. Wu (Eds.), Proceedings - 20th IEEE International Conference on Data Mining Workshops, ICDMW 2020 (pp. 762-765). Article 9346323 (IEEE International Conference on Data Mining Workshops, ICDMW; Vol. 2020-November). IEEE Computer Society. https://doi.org/10.1109/ICDMW51313.2020.00111

Explainable Anomaly Detection for District Heating Based on Shapley Additive Explanations. / Park, Sungwoo; Moon, Jihoon; Hwang, Eenjun.
Proceedings - 20th IEEE International Conference on Data Mining Workshops, ICDMW 2020. ed. / Giuseppe Di Fatta; Victor Sheng; Alfredo Cuzzocrea; Carlo Zaniolo; Xindong Wu. IEEE Computer Society, 2020. p. 762-765 9346323 (IEEE International Conference on Data Mining Workshops, ICDMW; Vol. 2020-November).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Park, S, Moon, J & Hwang, E 2020, Explainable Anomaly Detection for District Heating Based on Shapley Additive Explanations. in G Di Fatta, V Sheng, A Cuzzocrea, C Zaniolo & X Wu (eds), Proceedings - 20th IEEE International Conference on Data Mining Workshops, ICDMW 2020., 9346323, IEEE International Conference on Data Mining Workshops, ICDMW, vol. 2020-November, IEEE Computer Society, pp. 762-765, 20th IEEE International Conference on Data Mining Workshops, ICDMW 2020, Virtual, Sorrento, Italy, 20/11/17. https://doi.org/10.1109/ICDMW51313.2020.00111

Park S, Moon J, Hwang E. Explainable Anomaly Detection for District Heating Based on Shapley Additive Explanations. In Di Fatta G, Sheng V, Cuzzocrea A, Zaniolo C, Wu X, editors, Proceedings - 20th IEEE International Conference on Data Mining Workshops, ICDMW 2020. IEEE Computer Society. 2020. p. 762-765. 9346323. (IEEE International Conference on Data Mining Workshops, ICDMW). doi: 10.1109/ICDMW51313.2020.00111

Park, Sungwoo ; Moon, Jihoon ; Hwang, Eenjun. / Explainable Anomaly Detection for District Heating Based on Shapley Additive Explanations. Proceedings - 20th IEEE International Conference on Data Mining Workshops, ICDMW 2020. editor / Giuseppe Di Fatta ; Victor Sheng ; Alfredo Cuzzocrea ; Carlo Zaniolo ; Xindong Wu. IEEE Computer Society, 2020. pp. 762-765 (IEEE International Conference on Data Mining Workshops, ICDMW).

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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.",

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Explainable Anomaly Detection for District Heating Based on Shapley Additive Explanations

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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