文章基本信息

标题：A multi-scale time-series dataset with benchmark for machine learning in decarbonized energy grids
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作者：Xiangtian Zheng ; Nan Xu ; Loc trinh 等
期刊名称：Scientific Data
电子版ISSN：2052-4463
出版年度：2022
卷号：9
期号：1
页码：1-18
DOI：10.1038/s41597-022-01455-7
语种：English
出版社：Nature Publishing Group
摘要：the electric grid is a key enabling infrastructure for the ambitious transition towards carbon neutrality as we grapple with climate change . With deepening penetration of renewable resources, the reliable operation of the electric grid becomes increasingly challenging . In this paper, we present PSML, a frst-of-its-kind open-access multi-scale time-series dataset, to aid in the development of data-driven machine learning (ML)-based approaches towards reliable operation of future electric grids . The dataset is synthesized from a joint transmission and distribution electric grid to capture the increasingly important interactions and uncertainties of the grid dynamics, containing power, voltage and current measurements over multiple spatio-temporal scales . Using PSML, we provide state-of-the-art ML benchmarks on three challenging use cases of critical importance to achieve: (i) early detection, accurate classifcation and localization of dynamic disturbances; (ii) robust hierarchical forecasting of load and renewable energy; and (iii) realistic synthetic generation of physical-law-constrained measurements . We envision that this dataset will provide use-inspired ML research in safety-critical systems, while simultaneously enabling ML researchers to contribute towards decarbonization of energy sectors .