Semisupervised Learning-Based Occupancy Estimation for Real-Time Energy Management Using Ambient Data

Occupancy status information is essential for efficient energy management of deferrable and flexible electricity loads in both residential and commercial sectors. Motivated by the hardness to collect ground-truth occupancy data, we propose a new, semisupervised learning-based occupancy estimator, the label-expanded time-series propensity weighted estimator (LTPWE), to improve the prediction accuracy using privacy-preserving ambient sensing data (in temperature, humidity, light, etc.), especially when the labeling frequency (the ratio of the labeled positive instances) is low. The proposed energy management scheme integrates the LTPWE occupancy estimator into a data-driven deep reinforcement learning algorithm, the soft actor–critic (SAC), to make real-time scheduling decisions without any prior knowledge on the dynamics of random renewable generation and electricity price. Simulation results on real-world data sets show that compared with multiple state-of-the-art baselines, the proposed energy management scheme can reduce the energy cost by 18.79%–55.79% without sacrificing occupants’ thermal comfort.