Coupled building and system simulations for detection and diagnosis of high district heating return temperatures

Abstract

High return temperatures are a frequent issue leading to inefficiencies in district heating networks. The causes for high return temperatures usually lie on the secondary side, w i t h i n the building heating system. However, the district heating operator will in most cases only have access to primary side data through the heat meter. This makes it difficult for the operator to identify and remedy these causes. This contribution uses coupled building and system simulations to investigate issues leading to high return temperatures. The resulting synthetic data replace inaccessible secondary side data for the training of supervised classification algorithms allowing these issues to be diagnosed based on temperature and flow measurements in district heating substations. These classification algorithms are tested with three cases differing in the assumed availability of secondary side data. Fault detection and diagnosis can be performed with primary data only, with a modest degree of accuracy. Temperature measurements on the secondary side of the substation are shown to considerably improve the quality of predictions, from 78% to 96% classification accuracy.