Journal
SCIENTIFIC DATA
Volume 9, Issue 1, Pages -Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41597-022-01455-7
Keywords
-
Categories
Funding
- U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy (EERE) through the Solar Energy Technologies Office (SETO) [DE-EE0009031]
- National Science Foundation [OAC-1934675, ECCS-2035688, ECCS-1611301]
Ask authors/readers for more resources
This paper presents PSML, a first-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 captures the interactions and uncertainties of the grid dynamics and provides state-of-the-art ML benchmarks on three challenging use cases.
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 first-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 classification 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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available