  The forecasting of large ramps in wind power output known as ramp events is
crucial for the incorporation of large volumes of wind energy into national
electricity grids. Large variations in wind power supply must be compensated by
ancillary energy sources which can include the use of fossil fuels. Improved
prediction of wind power will help to reduce dependency on supplemental energy
sources along with their associated costs and emissions. In this paper, we
discuss limitations of current predictive practices and explore the use of
Machine Learning methods to enhance wind ramp event classification and
prediction. We additionally outline a design for a novel approach to wind ramp
prediction, in which high-resolution wind fields are incorporated to the
modelling of wind power.
