@inproceedings{63e4279c5e4a4764a6dc0e2a1ae6cba0,
title = "Data-Driven Generation of Synthetic Load Datasets Preserving Spatio-Temporal Features",
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.",
keywords = "generative models, historical, principal component analysis, singular value decomposition, spatio-temporal correlation, synthetic, time-series data",
author = "Andrea Pinceti and Oliver Kosut and Lalitha Sankar",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE Power and Energy Society General Meeting, PESGM 2019 ; Conference date: 04-08-2019 Through 08-08-2019",
year = "2019",
month = aug,
doi = "10.1109/PESGM40551.2019.8973532",
language = "English (US)",
series = "IEEE Power and Energy Society General Meeting",
publisher = "IEEE Computer Society",
booktitle = "2019 IEEE Power and Energy Society General Meeting, PESGM 2019",
}