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As the issue of freshwater shortage is increasing daily, it is critical totake effective measures for water conservation. According to previous studies,device level consumption could lead to significant freshwater conservation.Existing water disaggregation methods focus on learning the signatures forappliances; however, they are lack of the mechanism to accurately discriminateparallel appliances' consumption. In this paper, we propose a BayesianDiscriminative Sparse Coding model using Laplace Prior (BDSC-LP) to extensivelyenhance the disaggregation performance. To derive discriminative basisfunctions, shape features are presented to describe the low-sampling-rate waterconsumption patterns. A Gibbs sampling based inference method is designed toextend the discriminative capability of the disaggregation dictionaries.Extensive experiments were performed to validate the effectiveness of theproposed model using both real-world and synthetic datasets.

Water Disaggregation via Shape Features based Bayesian Discriminative Sparse Coding