智能电网中数据压缩的一种新的数据预处理方法
A New Approach of Data Pre-processing for Data Compression in Smart Grids
摘要：
用于压缩的预处理数据的常规方法是应用诸如傅立叶、Karhunen-Loève或小波变换的变换。采用这种方法的一个缺点是，它独立于压缩数据的使用，这可能会在根据最终效用测量时（而不是根据失真测量）引起显著的最优性损失。因此，我们通过将数据预处理操作泰勒化到使用压缩的（并且因此有噪声的）数据的决策实体的效用函数来重新审视该范例。更具体地说，效用函数由Lp范数组成，这在智能电网领域非常相关。线性和非线性的使用为导向的变换的设计和传统的数据预处理技术相比，压缩噪声的影响可以显着降低。
英文摘要：
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.
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