Abstract
In this paper, a novel characterization of smart meter data based on Gaussian mixture (GM) model is presented. It is shown that compared to the existing characterization models, the proposed GM model provides a significantly better fit for smart meter data. Furthermore, at each smart meter, sparsity of data is exploited to devise an adaptive data reduction algorithm using compressive sampling technique such that the bandwidth requirement for smart meter data transmission is reduced with minimum loss of information. When compared to the closest competitive scheme, the proposed compressive sampling based data reduction algorithm is found to be noise robust and offers ${\text{12.8}}$ % and ${\text{7.4}}$ % higher bandwidth saving, respectively, at 1 s and 30 s sampling intervals for comparable reconstruction accuracy. Proposed scheme is tested in real-time using RT-LAB.
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