TY - GEN
T1 - Explainable Anomaly Detection for District Heating Based on Shapley Additive Explanations
AU - Park, Sungwoo
AU - Moon, Jihoon
AU - Hwang, Eenjun
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/11
Y1 - 2020/11
N2 - 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.
AB - 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.
KW - anomaly detection
KW - differential pressure control valve
KW - district heating
KW - explainable artificial intelligence
KW - random forest
KW - shapley additive explanations
UR - http://www.scopus.com/inward/record.url?scp=85101387410&partnerID=8YFLogxK
U2 - 10.1109/ICDMW51313.2020.00111
DO - 10.1109/ICDMW51313.2020.00111
M3 - Conference contribution
AN - SCOPUS:85101387410
T3 - IEEE International Conference on Data Mining Workshops, ICDMW
SP - 762
EP - 765
BT - Proceedings - 20th IEEE International Conference on Data Mining Workshops, ICDMW 2020
A2 - Di Fatta, Giuseppe
A2 - Sheng, Victor
A2 - Cuzzocrea, Alfredo
A2 - Zaniolo, Carlo
A2 - Wu, Xindong
PB - IEEE Computer Society
T2 - 20th IEEE International Conference on Data Mining Workshops, ICDMW 2020
Y2 - 17 November 2020 through 20 November 2020
ER -
