Anomaly Detection of Heat Energy Usage in District Heating Substations Using LSTM based Variational Autoencoder Combined with Physical Model

Abstract

District heating systems that distribute heat through pipelines to residential and commercial buildings have been widely used in Northern Europe. The energy efficiency of district heating systems is of great interest to energy stakeholders. However, it is not uncommon that district heating systems fail to achieve the expected performance due to various faults. Identification of such rare observations that are different significantly from the majority of the meter readings data plays a vital role in system diagnose. In this study, a new hybrid approach is proposed for anomaly detection of a district heating substation, which consists of a simplified physical model and a Long Short Term Memory based Variational Autoencoder (LSTM VAE). A dataset of an anonymous substation in Sweden is used as a case study. The performance of two state of art models, LSTM and long short term memory based autoencoder (LSTM AE) are evaluated and compared with the LSTM VAE. Experimental results show that LSTM VAE outperforms the baseline models in terms of Area under receiver operating characteristic (ROC) curve (AUC) and F1 score when an optimal threshold is applied.