  Uncertainty analysis in the form of probabilistic forecasting can
significantly improve decision making processes in the smart power grid when
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. Traditionally quantile regression is applied for such forecasting
and recently quantile regression neural networks have become popular for
weather and renewable energy forecasting. However, one major shortcoming of
composite quantile estimation in neural networks is the quantile crossover
problem. This paper analyzes the effectiveness of a novel smoothed loss and
penalty function for neural network architectures to prevent the quantile
crossover problem. Its efficacy is examined on the wind power forecasting
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.
