mREAL-GAN: Generating Multiple Residential Electrical Appliance Load Profiles with Inter-Dependencies using a Generative Adversarial Network

TL;DR

This paper presents mREAL-GAN, a novel GAN model that generates multiple residential appliance load profiles simultaneously, capturing inter-dependencies for more accurate community-scale low-voltage network analysis.

Contribution

mREAL-GAN introduces an end-to-end, parallel generation approach for appliance load profiles, modeling inter-dependencies unlike previous independent methods.

Findings

mREAL-GAN outperforms previous methods in fidelity of individual appliance profiles

It effectively captures inter-dependencies between appliances

Demonstrates potential for improved community-scale energy analysis

Abstract

In this paper, we introduce mREAL-GAN, a generative adversarial network (GAN) for the parallel generation of multiple residential electrical appliance load (mREAL) profiles. mREAL-GAN is intended for use in community-scale low-voltage network analysis, and represents a departure from previous methods for this purpose, which break the generation of appliance load profiles into several steps and largely model each appliance independently. Instead, mREAL-GAN models appliance load profiles in an end-to-end manner, and generates multiple appliance load profiles in parallel in a way that captures inter-dependencies. We show that mREAL-GAN generates load profiles for individual appliance-types with greater fidelity than a popular example of previous methods, and demonstrate its ability to capture inter-dependencies between appliances.