EWELD: A Large-Scale Industrial and Commercial Load Dataset in Extreme Weather Events.

Autor: Liu G; School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, 518172, China.; Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, 518129, China., Liu J; School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, 518172, China., Bai Y; School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, 518172, China., Wang C; School of Data Science, The Chinese University of Hong Kong, Shenzhen, 518172, China., Wang H; School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, 518172, China., Zhao H; School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, 518172, China., Liang G; School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen, Shenzhen, 518055, China. lianggaoqi@hit.edu.cn., Zhao J; School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, 518172, China. zhaojunhua@cuhk.edu.cn.; Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, 518129, China. zhaojunhua@cuhk.edu.cn., Qiu J; School of Electrical and Information Engineering, The University of Sydney, Sydney, NSW, 2006, Australia.
Jazyk: angličtina
Zdroj: Scientific data [Sci Data] 2023 Sep 11; Vol. 10 (1), pp. 615. Date of Electronic Publication: 2023 Sep 11.
DOI: 10.1038/s41597-023-02503-6
Abstrakt: Load forecasting is crucial for the economic and secure operation of power systems. Extreme weather events, such as extreme heat and typhoons, can lead to more significant fluctuations in power consumption, making load forecasting more difficult. At present, due to the lack of relevant public data, the research on load forecasting under extreme weather events is still blank, so it is necessary to release a large-scale load dataset containing extreme weather events. The dataset includes electricity consumption data of industrial and commercial users under extreme weather events such as typhoons and extreme heat, which are collected at 15-minute intervals. The data is collected over six years from smart meters installed at the power entry points of users in southern China. The dataset consists of electricity consumption data from 386 industrial and commercial users in 17 industries, with more than 50 million records. During the recording period, extreme weather events such as typhoons and extreme heat are marked to form a total of 5,741 event records.
(© 2023. Springer Nature Limited.)
Databáze: MEDLINE
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