  We present a model for generating probabilistic forecasts by combining kernel
density estimation (KDE) and quantile regression techniques, as part of the
probabilistic load forecasting track of the Global Energy Forecasting
Competition 2014. The KDE method is initially implemented with a time-decay
parameter. We later improve this method by conditioning on the temperature or
the period of the week variables 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. Secondly
we combine three probabilistic forecasts with different weights for different
periods of the month.
