  Uncertainty analysis in the form of probabilistic forecasting can
significantly improve decision making processes in the smart power grid for
better integrating renewable energy sources such as wind. Whereas point
forecasting provides a single expected value, probabilistic forecasts provide
more information in the form of quantiles, prediction intervals, or full
predictive densities. This paper analyzes the effectiveness of a novel approach
for nonparametric probabilistic forecasting of wind power that combines a
smooth approximation of the pinball loss function with a neural network
architecture and a weighting initialization scheme to prevent the quantile
cross over problem. A numerical case study is conducted using publicly
available wind data from the Global Energy Forecasting Competition 2014.
Multiple quantiles are estimated to form 10%, to 90% prediction intervals which
are evaluated using a quantile score and reliability measures. Benchmark models
such as the persistence and climatology distributions, multiple quantile
regression, and support vector quantile regression are used for comparison
where results demonstrate the proposed approach leads to improved performance
while preventing the problem of overlapping quantile estimates.
