Robust Interactive HMI for Occupancy Estimation in Smart Buildings (WIP)

In this paper, we propose a Human-Machine Inter-face offering an efficient and privacy-conscious approach to train Occupancy Estimation Machine Learning models by interacting with users to request the occupancy level of a room. Although there are existing works that optimize the frequency of interactions, these techniques assume that the data is clean. Hence, anomalies in sensor measurements may lead to unnecessary interactions with users, resulting in their disengagement. To address this problem, we employ an Autoencoder neural network to detect anomalies in univariate time series sensor data using the reconstruction error. Furthermore, we tackle the autoencoder architecture selection challenge by utilizing a Reinforcement Learning-based Neural Architecture Search (RLNAS) approach, where an agent explores a predefined search space and identifies the optimal neural configuration by learning through trial and error. Experiments conducted on a custom anomaly detection dataset demonstrate competitive performance, and illustrate how this technique discovers effective architectures that may not be immediately apparent or intuitive.