A multi-scale time-series dataset with benchmark for machine learning in decarbonized energy grids

Xiangtian Zheng; Nan Xu; Loc Trinh; Dongqi Wu; Tong Huang; S. Sivaranjani; Yan Liu; Le Xie

doi:10.1038/s41597-022-01455-7

Scientific Data (Jun 2022)

A multi-scale time-series dataset with benchmark for machine learning in decarbonized energy grids

Xiangtian Zheng,
Nan Xu,
Loc Trinh,
Dongqi Wu,
Tong Huang,
S. Sivaranjani,
Yan Liu,
Le Xie

Affiliations

Xiangtian Zheng: Texas A&M University, Department of Electrical and Computer Engineering
Nan Xu: University of Southern California, Computer Science Department
Loc Trinh: University of Southern California, Computer Science Department
Dongqi Wu: Texas A&M University, Department of Electrical and Computer Engineering
Tong Huang: Massachusetts Institute of Technology, Laboratory for Information and Decision Systems
S. Sivaranjani: Purdue University, School of Industrial Engineering
Yan Liu: University of Southern California, Computer Science Department
Le Xie: Texas A&M University, Department of Electrical and Computer Engineering

DOI: https://doi.org/10.1038/s41597-022-01455-7
Journal volume & issue: Vol. 9, no. 1
pp. 1 – 18

Abstract

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Measurement(s) temperature • wind speed • solar zeinth angle • dew point • irradiance • voltage • current Technology Type(s) weather station • power grid model-based simulation Factor Type(s) load power • renewable generation power • disturbance location, type, and duration

Published in Scientific Data

ISSN: 2052-4463 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science
Website: https://www.nature.com/sdata/

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