A hybrid model of kernel density estimation and quantile regression for GEFCom2014 probabilistic load forecasting

We present a model for generating probabilistic forecasts that combines the kernel density estimation (KDE) and quantile regression techniques, as part of the probabilistic load forecasting track of the Global Energy Forecasting Competition 2014. Initially, the KDE method is implemented with a time-decay parameter, but we later improve this method by conditioning on the temperature or period of the week variables in order to provide more accurate forecasts. Secondly, we develop a simple but effective quantile regression forecast. The novel aspects of our methodology are two-fold. First, we introduce symmetry into the time-decay parameter of the kernel density estimation based forecast. Second, we combine three probabilistic forecasts with different weights for different periods of the month.