  As the issue of freshwater shortage is increasing daily, it is critical to
take 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 for
appliances; however, they are lack of the mechanism to accurately discriminate
parallel appliances' consumption. In this paper, we propose a Bayesian
Discriminative Sparse Coding model using Laplace Prior (BDSC-LP) to extensively
enhance the disaggregation performance. To derive discriminative basis
functions, shape features are presented to describe the low-sampling-rate water
consumption patterns. A Gibbs sampling based inference method is designed to
extend the discriminative capability of the disaggregation dictionaries.
Extensive experiments were performed to validate the effectiveness of the
proposed model using both real-world and synthetic datasets.
