@inproceedings{8c704b08abc0400bafeacc8ed6778fe8,
title = "Synthetic Time-Series Load Data via Conditional Generative Adversarial Networks",
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.",
keywords = "conditional generative adversarial networks, synthetic load data, time-series data",
author = "Andrea Pinceti and Lalitha Sankar and Oliver Kosut",
note = "Funding Information: This material is based upon work supported by the National Science Foundation under Grant Nos. CNS-1449080 Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE Power and Energy Society General Meeting, PESGM 2021 ; Conference date: 26-07-2021 Through 29-07-2021",
year = "2021",
doi = "10.1109/PESGM46819.2021.9637821",
language = "English (US)",
series = "IEEE Power and Energy Society General Meeting",
publisher = "IEEE Computer Society",
booktitle = "2021 IEEE Power and Energy Society General Meeting, PESGM 2021",
}