Abnormal data identification of central heating systems based on isolation forest algorithm

Aiming at the problem of heavy workload in abnormal data identification of central heating systems, this paper proposes to use the isolation forest (IF) algorithm to automatically identify abnormal data. Taking the data of a heating season in a heat exchange station in Tianjin as a sample, the physical laws of the central heating system data itself and the influence of the parameters set by the IF algorithm on the model performance are analysed in detail. Aiming at the problem of high misdiagnosis rate of some normal data caused by the operation regulation of central heating systems, a method of data set parameter relativization is proposed. This method can reduce the data misdiagnosis rate by 6.7% and the missed diagnosis rate by 44.6%. By comparing the model performance under different IF algorithm setting parameters, the recommended parameter setting range for abnormal data identification of the heating systems is given.