Title: Quantile Regression for Qualifying Match of GEFCom2017 Probabilistic Load Forecasting,
Abstract: We present a simple quantile regression-based forecasting method that was
applied in a probabilistic load forecasting framework of the Global Energy
Forecasting Competition 2017 (GEFCom2017). The hourly load data is log
transformed and split into a long-term trend component and a remainder term.
The key forecasting element is the quantile regression approach for the
remainder term that takes into account weekly and annual seasonalities such as
their interactions. Temperature information is only used to stabilize the
forecast of the long-term trend component. Public holidays information is
ignored. Still, the forecasting method placed second in the open data track and
fourth in the definite data track with our forecasting method, which is
remarkable given simplicity of the model. The method also outperforms the
Vanilla benchmark consistently.