Smart Building Management Application: Unsupervised First and Second-Order Moments Domain Adaptation for Occupant Behaviour Prediction Using IoT Sensor Data

Occupancy estimation (OE) and activity recognition (AR), predicted using Internet of Things (IoT) sensor data, are among the most important outcomes of artificial intelligence (AI) applied to buildings. The unavailability of enough labeled data is a serious problem for building researchers which is why they came up with domain adaptation (DA). Unsupervised domain adaptation (UDA) solves the problem of the unavailability of labeled data in target domains. In this research, we present a novel application for OE and AR that considers UDA methods. We have adapted 3 UDA methods called Joint Adaptation Networks (JAN), Deep Adaptation Networks (DAN), and Deep Correlation Alignment (D-CORAL). Our research application offers a practical solution for smart building management, enabling accurate OE and AR using adapted UDA techniques.