Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
HydroSense: infrastructure-mediated single-point sensing of whole-home water activity
Proceedings of the 11th international conference on Ubiquitous computing
The design of eco-feedback technology
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A longitudinal study of pressure sensing to infer real-world water usage events in the home
Pervasive'11 Proceedings of the 9th international conference on Pervasive computing
WaterSense: water flow disaggregation using motion sensors
Proceedings of the Third ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings
Signal Disaggregation via Sparse Coding with Featured Discriminative Dictionary
ICDM '12 Proceedings of the 2012 IEEE 12th International Conference on Data Mining
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The increasing demands on drinkable water, along with population growth, water-intensive agriculture and economic development, pose critical challenges to water sustainability. New techniques to long-term water conservation that incorporate principles of sustainability are expected. Recent studies have shown that providing customers with usage information of fixtures could help them save a considerable amount of water. Existing disaggregation techniques focus on learning consumption patterns for individual devices. Little attention has been given to the hierarchical decomposition structure of the aggregated consumption. In this paper, a Deep Sparse Coding based Recursive Disaggregation Model (DSCRDM) is proposed for water conservation. We design a recursive decomposition structure to perform the disaggregation task, and introduce sequential set to capture its characteristics. An efficient and effective algorithm deep sparse coding is developed to automatically learn the disaggregation architecture, along with discriminative and reconstruction dictionaries for each layer. We demonstrated that our proposed approach significantly improved the performance of the benchmark methods on a large scale disaggregation task and illustrated how our model could provide practical feedbacks to customers for water conservation.