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Deep Sparse Coding Based Recursive Disaggregation Model for Water Conservation

机译:基于深度稀疏编码的水源递归分解模型

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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.
机译:随着人口增长,水资源密集型农业和经济发展,对饮用水的需求不断增长,这对水的可持续性构成了严峻挑战。人们期望结合可持续性原则的长期节水新技术。最近的研究表明,为消费者提供水利用灯具的使用信息可以帮助他们节省大量的水。现有的分解技术着重于学习单个设备的消耗模式。很少关注聚集消耗的分层分解结构。提出了一种基于水的递归分解模型(DSCRDM),设计了递归分解结构来执行分解任务,并引入序列集以捕获其特征。开发了一种高效有效的深度稀疏编码算法来自动学习分解架构,以及每一层的判别词典和重建词典。我们证明了我们提出的方法显着提高了基准方法在大规模分类任务上的性能,并说明了我们的模型如何为节约用水向客户提供实用反馈。

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