A framework for efficient information aggregation in smart grid

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dc.contributor.author Joshi, Amit
dc.contributor.author Das, Laya
dc.contributor.author Natarajan, Balasubramaniam
dc.contributor.author Srinivasan, Babji
dc.date.accessioned 2019-06-19T11:12:57Z
dc.date.available 2019-06-19T11:12:57Z
dc.date.issued 2019-04
dc.identifier.citation Joshi, Amit; Das, Laya; Natarajan, Bala and Srinivasan, Babji, “A framework for efficient information aggregation in Smart Grid”, IEEE Transactions on Industrial Informatics, DOI: 10.1109/TII.2018.2866302, vol. 15, no. 4, pp. 2233-2243, Aug. 2018. en_US
dc.identifier.issn 1551-3203
dc.identifier.issn 1941-0050
dc.identifier.uri https://doi.org/10.1109/TII.2018.2866302
dc.identifier.uri https://repository.iitgn.ac.in/handle/123456789/4541
dc.description.abstract The two-way communication of information between agents in the smart grid, while making way for better monitoring and control, comes at the cost of elevated communication traffic. Compressive sensing is a technique that exploits sparsity of power consumption data (in the Haar basis) and achieves sub-Nyquist compression. Household power consumption data, however, have varying sparseness due to, for example, multistate appliances. Compressing this data with a fixed ratio can lead to nonoptimal results (less compression or large reconstruction error). In this regard, a dynamic compression scheme that estimates a signal's sparsity and decides the amount of compression is desirable. We demonstrate that this approach, when applied with existing estimators of sparsity, has its limitations in overemphasizing one objective compared to the other. We propose a new measure derived from coefficient of variation and demonstrate that it achieves a better tradeoff between reconstruction performance and compression ratio. In addition, we employ a dynamic spatial compression scheme to account for spatial correlation between data of neighboring nodes and present a framework that incorporates dynamic temporal and spatial compression. We present the results on three publicly available datasets at different sampling rates and outline key findings of the study.
dc.description.statementofresponsibility by Amit Joshi, Laya Das, Balasubramaniam Natarajan and Babji Srinivasan
dc.format.extent vol. 15, no. 4, pp. 2233-2243
dc.language.iso en en_US
dc.publisher IEEE en_US
dc.subject Smart grids en_US
dc.subject Compressed sensing en_US
dc.subject Power demand en_US
dc.subject Indexes en_US
dc.subject Informatics en_US
dc.subject Sensors en_US
dc.subject Power measurement en_US
dc.title A framework for efficient information aggregation in smart grid en_US
dc.type Article en_US
dc.relation.journal IEEE Transactions on Industrial Informatics


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