  The conventional approach to pre-process data for compression is to apply
transforms such as the Fourier, the Karhunen-Lo\`{e}ve, 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.
