Anomaly Detection for Multi-Zone Buildings Using Cluster-Trained LSTM Autoencoders

The optimal energy performance of building operations is affected by component faults, which may go unnoticed for long periods of time. Significant energy savings can be achieved if faulty behaviors are detected and rectified in a timely manner. In this work, we adopt an unsupervised approach for anomaly detection that combines automatic data engineering using clustering methods with Long-Short Term Memory (LSTM)-based Autoencoders. First, data engineering is used to extract multiple operating modes from nominal data of building operations. Then, an LSTM-based Autoencoder is trained to capture the characteristics of non-linear and temporal dynamics for each operating mode. Finally, the ensemble of models can be used for anomaly detection after training has been completed. We benchmark variants of our approach against state-of-the-art Autoencoders for anomaly detection by using a recently developed experimental dataset provided by the ASHRAE Research Project RP-1312. Unsupervised anomaly detection is a challenging task due to the lack of faulty labels and the need to identify faults while avoiding false alarms. Our novel approach improves the average true positive rate for fault detection by 11.4% against a state-of-the-art plain LSTM Autoencoder while keeping the false alarm rate around 5 % without having to use labeled fault data.