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.

中文翻译：

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智能电网中高效信息聚合的框架

智能电网中的代理之间的信息双向通信在为更好的监视和控制铺平道路的同时，却以通信流量的增加为代价。压缩感测是一种利用功率消耗数据稀疏性（以Haar为基础）并实现次奈奎斯特压缩的技术。但是，由于例如多状态设备，家庭功耗数据的稀疏程度有所不同。以固定比例压缩此数据可能会导致结果不理想（压缩程度较小或重建误差较大）。在这方面，期望一种动态压缩方案，该方案估计信号的稀疏度并确定压缩量。我们证明，这种方法与现有的稀疏度估算器一起使用时，在过分强调一个目标与另一个目标方面有其局限性。我们提出了一种从变异系数得出的新指标，并证明了它在重建性能和压缩率之间取得了较好的折衷。此外，我们采用动态空间压缩方案来解决相邻节点数据之间的空间相关性，并提出了一个包含动态时间和空间压缩的框架。我们以不同的采样率在三个可公开获得的数据集上呈现结果，并概述了这项研究的主要发现。我们采用动态空间压缩方案来解决相邻节点数据之间的空间相关性，并提出了一个包含动态时间和空间压缩的框架。我们以不同的采样率在三个可公开获得的数据集上呈现结果，并概述了这项研究的主要发现。我们采用动态空间压缩方案来解决相邻节点数据之间的空间相关性，并提出了一个包含动态时间和空间压缩的框架。我们以不同的采样率在三个可公开获得的数据集上呈现结果，并概述了这项研究的主要发现。