气候变化领域集成服务门户(CCPortal): Variational data augmentation for a learning-based granular predictive model of

CCPortal

DOI	10.1016/j.epsr.2024.110299
	Variational data augmentation for a learning-based granular predictive model of
	Zhao, Tianqiao; Yue, Meng; Jensen, Michael; Endo, Satoshi; Marschilok, Amy C.; Nugent, Brian; Cerruti, Brian; Spanos, Constantine
发表日期	2024
ISSN	0378-7796
EISSN	1873-2046
起始页码	232
卷号	232
英文摘要	As the trend in climate change continues, extreme weather events are expected to occur with increasing frequency and severity and pose a significant threat to the electric power infrastructure. Regardless of the efforts a utility puts towards hardening the grid, storm-induced damage to the utility assets such as cables and distributed energy resources (DERs) that are particularly vulnerable to such events is unavoidable. Access to a highly granular, in space and time, outage forecasting tool with long lead times (i.e., days ahead) will enhance the efficiency of service restoration efforts. In this study, we propose to develop and implement a multi -model framework as an operational tool based on a granular and multi -day outage forecasting model using operational numerical weather prediction model forecasts and detailed component outage information. An innovative two-layered recurrent neural network, i.e., a long-short-term-memory (LSTM)-based variational autoencoder (VAE) framework and a sliding window are used to address the uneven distribution of different types of weather events and make better use of the time -series data. Case studies are performed to demonstrate the performance of the new framework.
英文关键词	Variational data augmentation; Outage prediction; Variational autoencoder; Recurrent neural networks; Weather-related outages
语种	英语
WOS研究方向	Engineering
WOS类目	Engineering, Electrical & Electronic
WOS记录号	WOS:001235050800001
来源期刊	ELECTRIC POWER SYSTEMS RESEARCH
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/293975
作者单位	United States Department of Energy (DOE); Brookhaven National Laboratory
推荐引用方式 GB/T 7714	Zhao, Tianqiao,Yue, Meng,Jensen, Michael,et al. Variational data augmentation for a learning-based granular predictive model of[J],2024,232.
APA	Zhao, Tianqiao.,Yue, Meng.,Jensen, Michael.,Endo, Satoshi.,Marschilok, Amy C..,...&Spanos, Constantine.(2024).Variational data augmentation for a learning-based granular predictive model of.ELECTRIC POWER SYSTEMS RESEARCH,232.
MLA	Zhao, Tianqiao,et al."Variational data augmentation for a learning-based granular predictive model of".ELECTRIC POWER SYSTEMS RESEARCH 232(2024).