BigDEAL Challenge 2022: Forecasting peak timing of electricity demand

被引:1
|
作者
Shukla, Shreyashi [1 ]
Hong, Tao [1 ,2 ]
机构
[1] Univ N Carolina, Charlotte, NC USA
[2] 9201 Univ City Blvd, Charlotte, NC 28223 USA
关键词
data analysis; energy demand management; load forecasting; LOAD; GENERATION; ANNSTLF;
D O I
10.1049/stg2.12162
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Peak load forecasting is crucial to power system planning and operations. While the literature has reported many studies on forecasting the magnitude of peak load, few have focused on the timing aspect. In the fall of 2022, the Big Data Energy Analytics Laboratory (BigDEAL) organised the BigDEAL Challenge 2022, which was devoted to short-term ex-ante peak timing forecasting. The competition attracted 78 teams formed by 121 contestants from 27 countries. The authors introduce the competition in detail, including its precursor competitions held in the 2010s, the framework and setup, and a summary of the methods used by the participants. The authors also publish the data of the BigDEAL Challenge 2022 along with this paper. Lastly, the authors present their perspective on the research challenges of peak timing forecasting and future load forecasting competitions. BigDEAL Challenge 2022, which was devoted to short-term ex-ante peak timing forecasting, attracted 78 teams formed by 121 contestants from 27 countries. The authors introduce the competition in detail, including its precursor competitions held in the 2010s, the framework and setup, and a summary of the methods used by the participants. image
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页码:442 / 459
页数:18
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