Energy Disaggregation with Semi-supervised Sparse Coding

• URL: http://arxiv.org/abs/2004.10529v4
• Date: Mon, 13 Jul 2020 16:07:59 GMT
• Title: Energy Disaggregation with Semi-supervised Sparse Coding
• Authors: Mengheng Xue, Samantha Kappagoda and David K. A. Mordecai
• Abstract summary: Energy disaggregation research aims to decompose the aggregated energy consumption data into its component appliances. In this paper, a discriminative disaggregation model based on sparse coding has been evaluated on large-scale household power usage dataset for energy conservation.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Residential smart meters have been widely installed in urban houses nationwide to provide efficient and responsive monitoring and billing for consumers. Studies have shown that providing customers with device-level usage information can lead consumers to economize significant amounts of energy, while modern smart meters can only provide informative whole-home data with low resolution. Thus, energy disaggregation research which aims to decompose the aggregated energy consumption data into its component appliances has attracted broad attention. In this paper, a discriminative disaggregation model based on sparse coding has been evaluated on large-scale household power usage dataset for energy conservation. We utilize a structured prediction model for providing discriminative sparse coding training, accordingly, maximizing the energy disaggregation performance. Designing such large scale disaggregation task is investigated analytically, and examined in the real-world smart meter dataset compared with benchmark models.

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