Data-Driven Generation of Synthetic Load Datasets Preserving Spatio-Temporal Features

Andrea Pinceti, Oliver Kosut, Lalitha Sankar

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

1 Scopus citations

Abstract

A generative model for the creation of realistic historical bus-level load data for transmission grid models is presented. A data-driven approach based on principal component analysis is used to learn the spatio-temporal correlation between the loads in a system and build a generative model. Given a system topology and a set of base case loads, individual, realistic time-series data for each load can be generated. This technique is demonstrated by learning from a large proprietary dataset and generating historical data for the 2383-bus Polish test case.

Original languageEnglish (US)
Title of host publication2019 IEEE Power and Energy Society General Meeting, PESGM 2019
PublisherIEEE Computer Society
ISBN (Electronic)9781728119816
DOIs
StatePublished - Aug 2019
Event2019 IEEE Power and Energy Society General Meeting, PESGM 2019 - Atlanta, United States
Duration: Aug 4 2019Aug 8 2019

Publication series

NameIEEE Power and Energy Society General Meeting
Volume2019-August
ISSN (Print)1944-9925
ISSN (Electronic)1944-9933

Conference

Conference2019 IEEE Power and Energy Society General Meeting, PESGM 2019
CountryUnited States
CityAtlanta
Period8/4/198/8/19

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Keywords

  • generative models
  • historical
  • principal component analysis
  • singular value decomposition
  • spatio-temporal correlation
  • synthetic
  • 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

Cite this

Pinceti, A., Kosut, O., & Sankar, L. (2019). Data-Driven Generation of Synthetic Load Datasets Preserving Spatio-Temporal Features. In 2019 IEEE Power and Energy Society General Meeting, PESGM 2019 [8973532] (IEEE Power and Energy Society General Meeting; Vol. 2019-August). IEEE Computer Society. https://doi.org/10.1109/PESGM40551.2019.8973532