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

TL;DR

ProfileSR-GAN is a novel GAN-based framework that enhances low-resolution load profiles to high-resolution, capturing high-frequency details lost during down-sampling, thereby improving analysis accuracy in power systems.

Contribution

The paper introduces ProfileSR-GAN, a new GAN-based method with shape-related losses and metrics for super-resolving load profiles, outperforming traditional methods.

Findings

Outperforms MSE-based methods in shape metrics

Generates realistic high-frequency load profiles

Effective in non-intrusive load monitoring (NILM) applications

Abstract

It is a common practice for utilities to down-sample smart meter measurements from high resolution (e.g. 1-min or 1-sec) to low resolution (e.g. 15-, 30- or 60-min) to lower the data transmission and storage cost. However, down-sampling can remove high-frequency components from time-series load profiles, making them unsuitable for in-depth studies such as quasi-static power flow analysis or non-intrusive load monitoring (NILM). Thus, in this paper, we propose ProfileSR-GAN: a Generative Adversarial Network (GAN) based load profile super-resolution (LPSR) framework for restoring high-frequency components lost through the smoothing effect of the down-sampling process. The LPSR problem is formulated as a Maximum-a-Prior problem. When training the ProfileSR-GAN generator network, to make the generated profiles more realistic, we introduce two new shape-related losses in addition to conventionally used content loss: adversarial loss and feature-matching loss. Moreover, a new set of shape-based evaluation metrics are proposed to evaluate the realisticness of the generated profiles. Simulation results show that ProfileSR-GAN outperforms Mean-Square Loss based methods in all shape-based metrics. The successful application in NILM further demonstrates that ProfileSR-GAN is effective in recovering high-resolution realistic waveforms.