TY - JOUR
T1 - Comparative assessment of generative models for transformer- and consumer-level load profiles generation
AU - Xia, Weijie
AU - Huang, Hanyue
AU - Duque, Edgar Mauricio Salazar
AU - Hou, Shengren
AU - Palensky, Peter
AU - Vergara, Pedro P.
PY - 2024
Y1 - 2024
N2 - Residential load profiles (RLPs) play an increasingly important role in the optimal operation and planning of distribution systems, particularly with the rising integration of low-carbon energy resources such as PV systems, electric vehicles, small-scale batteries, etc. Despite the prevalence of various data-driven models for generating consumption profiles, there is a lack of clear conclusions about their relative strengths and weaknesses. This study undertakes a comprehensive comparison of frequently used data-driven models in recent research, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAE), Wasserstein GANs (WGAN), WGANs with Gradient Penalty (WGANGP), Gaussian Mixture Models (GMMs), and Gaussian Mixture Copulas (GMC). The presented comparison explores the effectiveness of the above-mentioned models on transformer- and consumer-level consumption profiles, as well as for different time resolutions (15-min, 30-min, and 60-min). The objective of this research is to elucidate the respective advantages and drawbacks of these models, thereby providing valuable insights for subsequent research in this field.
AB - Residential load profiles (RLPs) play an increasingly important role in the optimal operation and planning of distribution systems, particularly with the rising integration of low-carbon energy resources such as PV systems, electric vehicles, small-scale batteries, etc. Despite the prevalence of various data-driven models for generating consumption profiles, there is a lack of clear conclusions about their relative strengths and weaknesses. This study undertakes a comprehensive comparison of frequently used data-driven models in recent research, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAE), Wasserstein GANs (WGAN), WGANs with Gradient Penalty (WGANGP), Gaussian Mixture Models (GMMs), and Gaussian Mixture Copulas (GMC). The presented comparison explores the effectiveness of the above-mentioned models on transformer- and consumer-level consumption profiles, as well as for different time resolutions (15-min, 30-min, and 60-min). The objective of this research is to elucidate the respective advantages and drawbacks of these models, thereby providing valuable insights for subsequent research in this field.
KW - Consumption profiles
KW - Distribution network
KW - Generative adversarial networks
KW - Generative models
UR - http://www.scopus.com/inward/record.url?scp=85187217625&partnerID=8YFLogxK
U2 - 10.1016/j.segan.2024.101338
DO - 10.1016/j.segan.2024.101338
M3 - Article
AN - SCOPUS:85187217625
SN - 2352-4677
VL - 38
JO - Sustainable Energy, Grids and Networks
JF - Sustainable Energy, Grids and Networks
M1 - 101338
ER -