Explainable Anomaly Detection for District Heating Based on Shapley Additive Explanations

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.