Global Energy Forecasting Competition 2017: Hierarchical Probabilistic Load Forecasting

Check out the winning methodologies and data used in GEFCom2017! If you don't have access to ScienceDirect, you can use the dropbox link below to access the data.

Citation

Tao Hong, Jingrui Xie, and Jonathan Black, "Global Energy Forecasting Competition 2017: Hierarchical Probabilistic Load Forecasting," International Journal of Forecasting, in press. (ScienceDirect; Data)

Global Energy Forecasting Competition 2017: Hierarchical Probabilistic Load Forecasting

Tao Hong, Jingrui Xie, and Jonathan Black

Abstract

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.