Large-scale monitoring of operationally diverse district heating substations: A reference-group based approach

Abstract

A typical district heating (DH) network consists of hundreds, sometimes thousands, of substations. In the absence of a well-understood prior model or data labels about each substation, the overall monitoring of such large number of substations can be challenging. To overcome the challenge, an approach based on the collective operational monitoring of each substation by a local group (i.e., the reference-group) of other similar substations in the network was formulated. Herein, if a substation of interest (i.e., the target) starts to behave differently in comparison to those in its reference-group, then it was designated as an outlier. The approach was demonstrated on the monitoring of the return temperature variable for atypical11Here, “atypical” means that while it does not fit the definition of a normal operation, it is not faulty either and may also have some context. and faulty operational behavior in 778 substations associated with multi-dwelling buildings. The choice of an appropriate similarity measure along with its size k were the two important factors that enables a reference-group to effectively detect an outlier target. Thus, different similarity measures and size k for the construction of the reference-groups were investigated, which led to the selection of the Euclidean distance with k=80. This setup resulted in the detection of 77 target substations that were outliers, i.e., the behavior of their return temperature changed in comparison to the majority of those in their respective reference-groups. Of these, 44 were detected due to the local construction of the reference-groups. In addition, six frequent patterns of deviating behavior in the return temperature of the substations were identified using the reference-group based approach, which were then further corroborated by the feedback from a DH domain expert.