The scarcity of potable water is a critical challenge in many regions around the world. Previous studies have shown that knowledge of device-level water usage can lead to significant conservation. Although there is considerable interest in determining discriminative features via sparse coding for water disaggregation to separate whole-house consumption into its component appliances, existing methods lack a mechanism for fitting coefficient distributions and are thus unable to accurately discriminate parallel devices’ consumption. This article proposes a Bayesian discriminative sparse coding model, referred to as Virtual Metering (VM), for this disaggregation task. Mixture-of-Gammas is employed for the prior distribution of coefficients, contributing two benefits: (i) guaranteeing the coefficients’ sparseness and non-negativity, and (ii) capturing the distribution of active coefficients. The resulting method effectively adapts the bases to aggregated consumption to facilitate discriminative learning in the proposed model, and devices’ shape features are formalized and incorporated into Bayesian sparse coding to direct the learning of basis functions. Compact Gibbs Sampling (CGS) is developed to accelerate the inference process by utilizing the sparse structure of coefficients. The empirical results obtained from applying the new model to large-scale real and synthetic datasets revealed that VM significantly outperformed the benchmark methods.

This paper addresses geospatial interpolation for meteorological measurements in which we estimate the values of climatic metrics at unsampled sites with existing observations. Providing climatological and meteorological conditions covering a large region is potentially useful in many applications, such as smart grid. However, existing research works on interpolation either cause a large number of complex calculations or are lack of high accuracy. We propose a Bayesian compressed sensing based non-parametric statistical model to efficiently perform the spatial interpolation task. Student-t priors are employed to model the sparsity of unknown signals' coefficients, and the Approximated Variational Inference (AVI) method is provided for effective and fast learning. The presented model has been deployed at IBM, targeting for aiding the intelligent management of smart grid. The evaluations on two real world datasets demonstrate that our algorithm achieves state-of-the-art performance in both effectiveness and efficiency.

The Probability Density Function (PDF) is the fundamental data model for a variety of stream mining algorithms. Existing works apply the standard nonparametric Kernel Density Estimator (KDE) to approximate the PDF of data streams. As a result, the stream-based KDEs cannot accurately capture complex local density features. In this article, we propose the use of Local Region (LRs) to model local density information in univariate data streams. In-depth theoretical analyses are presented to justify the effectiveness of the LR-based KDE. Based on the analyses, we develop the General Local rEgion AlgorithM (GLEAM) to enhance the estimation quality of structurally complex univariate distributions for existing stream-based KDEs. A set of algorithmic optimizations is designed to improve the query throughput of GLEAM and to achieve its linear order computation. Additionally, a comprehensive suite of experiments was conducted to test the effectiveness and efficiency of GLEAM.

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.

Activity analysis disaggregates utility consumption from smart meters into specific usage that associates with human activities. It can not only help residents better manage their consumption for sustainable lifestyle, but also allow utility managers to devise conservation programs. Existing research efforts on disaggregating consumption focus on analyzing consumption features with high sample rates (mainly between 1 Hz ~ 1MHz). However, many smart meter deployments support sample rates at most 1/900 Hz, which challenges activity analysis with occurrences of parallel activities, difficulty of aligning events, and lack of consumption features. We propose a novel statistical framework for disaggregation on coarse granular smart meter readings by modeling fixture characteristics, household behavior, and activity correlations. This framework has been implemented into two approaches for different application scenarios, and has been deployed to serve over 300 pilot households in Dubuque, IA. Interesting activity-level consumption patterns have been identified, and the evaluation on both real and synthetic datasets has shown high accuracy on discovering washer and shower.