SSIOE: Self-Supervised Indoor Occupancy Estimation for Intelligent Building Management

This paper aims to estimate indoor occupancy given the real-time observed signals from the existing sensors; next, as a practical application, we build a dynamic control schedule for energy saving based on the estimated indoor occupancy. However, several issues need to be addressed. First, it is impossible to train the model with rich labels due to the expensive labeling cost. Second, manual annotation of the continuous occupancy rate is complex. Third, the mapping relationship between sensor data and occupancy will change in the long run. In this paper, we proposed a new algorithm named Self-Supervised Indoor Occupancy Estimation (SSIOE) to overcome the challenges. Specifically, our training scheme aims to (I) generate a set of pseudo labels in a simple way to mark the time periods believed to be either in a high or low occupancy state and (II) utilize these sparse labels for training a network to infer the continuous occupancy ratio. By reformulating the problem as a Wasserstein-distance like estimation, SSIOE is a novel learning-based method that can rely only on the weak/sparse labels of either "high-occupancy" or "low-occupancy" and learn to estimate the continuous occupancy ratio. Furthermore, to deal with the scarce annotation problem, we proposed a novel physical constraint loss to model the physical prior. (III) Last but not least, to strengthen the adaptability, we integrated Model-Agnostic Meta-Learning (MAML) to train the model for dynamic model updating. Experimental results show that SSIOE can provide reliable occupancy estimation and flexibly adapt to various control modes without retraining the model.