SHARK: sparse human action recovery with knowledge of appliances and load curve data

Occupancy detection can greatly facilitate heating, ventilation and cooling and lightning control for building energy saving. Sensor-based occupancy detection is usually costly and may suffer from high false positive rates. As such, occupancy detection using load curve data has been proposed. Such methods, however, normally (i) rely on tedious and nontrivial model training process and (ii) do not consider the influence of corrupted data in load curve. To overcome these pitfalls, we develop a practical, robust non-intrusive occupancy detection approach that does not require model training and data cleansing. Only using load curve data and readily available appliance knowledge, the method achieves occupancy detection by three main steps: (i) the appliances’ mode states are firstly decoded via a carefully designed robust sparse switching event recovering model; (ii) the human actions are recovered with a priori knowledge of human-activated switching events; (iii) the occupancy states are then inferred based on the recovered human actions along with empirical strategies and association rules. We evaluate our approach and compare it with existing methods with real-world data. The results show that our approach can achieve similar performance to those using supervised machine learning.