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 real-world dataset demonstrated the effectiveness of the proposed approach.

This paper presents an integrated framework for Spatio-Temporal-Textual (STT) information retrieval and knowledge discovery system. The proposed ensemble framework contains an efficient STT search engine with multiple indexing, ranking and scoring schemes, an effective STT pattern miner with Spatio-Temporal (ST) analytics, and novel STT topic modeling. Specifically, we design an effective prediction prototype with a third-order linear regression model, and present an innovative STT topic modeling relevance ranker to score documents based on inherent STT features under topical space. We demonstrate the framework with a crime dataset from the Washington, DC area from 2006 to 2010 and a global terrorism dataset from 2004 to 2010.

As the issue of freshwater shortage is increasing daily, it's critical to take effective measures for water conservation. Based on previous studies, device level consumption could lead to significant conservation of freshwater. However, current smart meter deployments only produce low sample rate aggregated data. In this paper, we examine the task of separating whole-home water consumption into its component appliances. A key challenge is to address the unique features of low sample rate data. To this end, we propose Sparse Coding with Featured Discriminative Dictionary (SCFDD) by incorporating inherent shape and activation features to capture the discriminative characteristics of devices. In addition, extensive experiments were performed to validate the effectiveness of SCFDD.