Synthetic Time-Series Load Data via Conditional Generative Adversarial Networks

Andrea Pinceti, Lalitha Sankar, Oliver Kosut

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A framework for the generation of synthetic time-series transmission-level load data is presented. Conditional generative adversarial networks are used to learn the patterns of a real dataset of hourly-sampled week-long load profiles and generate unique synthetic profiles on demand, based on the season and type of load required. Extensive testing of the generative model is performed to verify that the synthetic data fully captures the characteristics of real loads and that it can be used for downstream power system and/or machine learning applications.

Original languageEnglish (US)
Title of host publication2021 IEEE Power and Energy Society General Meeting, PESGM 2021
PublisherIEEE Computer Society
ISBN (Electronic)9781665405072
DOIs
StatePublished - 2021
Event2021 IEEE Power and Energy Society General Meeting, PESGM 2021 - Washington, United States
Duration: Jul 26 2021Jul 29 2021

Publication series

NameIEEE Power and Energy Society General Meeting
Volume2021-July
ISSN (Print)1944-9925
ISSN (Electronic)1944-9933

Conference

Conference2021 IEEE Power and Energy Society General Meeting, PESGM 2021
Country/TerritoryUnited States
CityWashington
Period7/26/217/29/21

Keywords

  • conditional generative adversarial networks
  • synthetic load data
  • time-series data

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Nuclear Energy and Engineering
  • Renewable Energy, Sustainability and the Environment
  • Electrical and Electronic Engineering

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