Global energy forecasting competition 2017: Hierarchical probabilistic load forecasting

被引:125
作者
Hong, Tao [1 ]
Xie, Jingrui [2 ]
Black, Jonathan [3 ]
机构
[1] Univ N Carolina, Dept Syst Engn & Engn Management, Charlotte, NC 28223 USA
[2] SAS Inst Inc, Forecasting R&D, Cary, NC USA
[3] ISO New England, Holyoke, MA USA
关键词
Load forecasting; Hierarchical forecasting; Forecasting competition; Energy forecasting; Probabilistic forecasting;
D O I
10.1016/j.ijforecast.2019.02.006
中图分类号
F [经济];
学科分类号
02 ;
摘要
The Global Energy Forecasting Competition 2017 (GEFCom2017) attracted more than 300 students and professionals from over 30 countries for solving hierarchical probabilistic load forecasting problems. Of the series of global energy forecasting competitions that have been held, GEFCom2017,is the most challenging,one to date: the first one to have a qualifying match, the first one to use hierarchical data with more than two levels, the first one to allow the usage of external data sources, the first one to ask for real-time ex-ante forecasts, and the longest one. This paper introduces the qualifying and final matches of GEFCom2017, summarizes the top-ranked methods, publishes the data used in the competition, and presents several reflections on the competition series and a vision for future energy forecasting competitions. (C) 2019 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
引用
收藏
页码:1389 / 1399
页数:11
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