论文标题
智能网格中数据压缩的数据预处理的新方法
A New Approach of Data Pre-processing for Data Compression in Smart Grids
论文作者
论文摘要
预压预处理数据的常规方法是应用转换，例如傅立叶，karhunen-loève或小波变换。采用这种方法的一个缺点是，它独立于使用压缩数据的使用，这可能会在最终效用方面衡量时会引起重大的最佳损失（而不是根据失真进行测量）。因此，我们通过使用压缩的（以及嘈杂的）数据将数据预处理操作划分为决策实体的实用性功能来重新审视此范式。更具体地说，实用程序功能由LP-Norm组成，该功能在智能电网领域非常相关。线性和非线性用途的转换均已设计并与常规数据预处理技术进行了比较，表明压缩噪声的影响可以大大降低。
The conventional approach to pre-process data for compression is to apply transforms such as the Fourier, the Karhunen-Loève, or wavelet transforms. One drawback from adopting such an approach is that it is independent of the use of the compressed data, which may induce significant optimality losses when measured in terms of final utility (instead of being measured in terms of distortion). We therefore revisit this paradigm by tayloring the data pre-processing operation to the utility function of the decision-making entity using the compressed (and therefore noisy) data. More specifically, the utility function consists of an Lp-norm, which is very relevant in the area of smart grids. Both a linear and a non-linear use-oriented transforms are designed and compared with conventional data pre-processing techniques, showing that the impact of compression noise can be significantly reduced.
