A Hierarchical Probabilistic Model for Low Sample Rate Home-Use Energy Disaggregation

Energy crisis and climate change have caused a global concern and motivated efforts to reduce energy consumption. Studies have shown that providing appliance-level consumption information can help users conserve a significant amount of energy. Existing methods focus on learning parallel signal signatures, but the inherent relationships between the signatures have not been well explored. This paper presents the Hierarchical Probabilistic Model for Energy Disaggregation (HPMED). We derive the discriminative features from low sample rate power readings to characterise device functional modes. The HPMED model bridges the discriminative features, working states, and aggregated consumption. To address the analytical intractable problem, an efficient algorithm is proposed to approximately infer the latent states for disaggregation task. Extensive experiments on a realworld dataset demonstrated the effectiveness of the proposed