  Uncertainty analysis in the form of probabilistic forecasting can provide
significant improvements in decision-making processes in the smart power grid
for better integrating renewable energies 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 an approach for
nonparametric probabilistic forecasting of wind power that combines support
vector machines and nonlinear quantile regression with non-crossing
constraints. A numerical case study is conducted using publicly available wind
data from the Global Energy Forecasting Competition 2014. Multiple quantiles
are estimated to form 20%, 40%, 60% and 80% prediction intervals which are
evaluated using the pinball loss function and reliability measures. Three
benchmark models are used for comparison where results demonstrate the proposed
approach leads to significantly better performance while preventing the problem
of overlapping quantile estimates.
