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It is a common practice for utilities to down-sample smart meter measurementsfrom high resolution (e.g. 1-min or 1-sec) to low resolution (e.g. 15-, 30- or60-min) to lower the data transmission and storage cost. However, down-samplingcan remove high-frequency components from time-series load profiles, makingthem unsuitable for in-depth studies such as quasi-static power flow analysisor non-intrusive load monitoring (NILM). Thus, in this paper, we proposeProfileSR-GAN: a Generative Adversarial Network (GAN) based load profilesuper-resolution (LPSR) framework for restoring high-frequency components lostthrough the smoothing effect of the down-sampling process. The LPSR problem isformulated as a Maximum-a-Prior problem. When training the ProfileSR-GANgenerator network, to make the generated profiles more realistic, we introducetwo new shape-related losses in addition to conventionally used content loss:adversarial loss and feature-matching loss. Moreover, a new set of shape-basedevaluation metrics are proposed to evaluate the realisticness of the generatedprofiles. Simulation results show that ProfileSR-GAN outperforms Mean-SquareLoss based methods in all shape-based metrics. The successful application inNILM further demonstrates that ProfileSR-GAN is effective in recoveringhigh-resolution realistic waveforms.

ProfileSR-GAN: A GAN based Super-Resolution Method for Generating High-Resolution Load Profiles