Neural Architecture Search for Anomaly Detection in Time-Series Data of Smart Buildings: A Reinforcement Learning Approach for Optimal Autoencoder Design

The proliferation of Internet of Things (IoT) sensors in smart buildings has generated vast amounts of time-series data, offering valuable insights when properly leveraged. We propose to use this data to identify abnormal behaviors and deviations in temporal data which will enable the detection of anomalies related to power consumption, control system failures, and sensor malfunctions. To achieve this, we propose a reconstruction-based anomaly detection framework utilizing autoencoders where we train the model on anomaly-free samples, minimizing the error between the original and reconstructed sequences. Then, by setting a threshold on the reconstruction error, abnormal sequences can be distinguished from the predominant regular patterns observed in the majority of the time windows. Moreover, to address the challenge of selecting a suitable autoencoder architecture, a reinforcement learning-based neural architecture search (RLNAS) approach is employed to explore a manually defined search space and discover the best neural configuration by learning through trial and error. Experimental results on two custom anomaly detection data sets demonstrate competitive performance, showcasing the effectiveness of this approach in discovering effective architectures that may not be immediately apparent or intuitive.