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We present a model for generating probabilistic forecasts by combining kerneldensity estimation (KDE) and quantile regression techniques, as part of theprobabilistic load forecasting track of the Global Energy ForecastingCompetition 2014. The KDE method is initially implemented with a time-decayparameter. We later improve this method by conditioning on the temperature orthe period of the week variables to provide more accurate forecasts. Secondly,we develop a simple but effective quantile regression forecast. The novelaspects of our methodology are two-fold. First, we introduce symmetry into thetime-decay parameter of the kernel density estimation based forecast. Secondlywe combine three probabilistic forecasts with different weights for differentperiods of the month.

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