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The forecasting of large ramps in wind power output known as ramp events iscrucial for the incorporation of large volumes of wind energy into nationalelectricity grids. Large variations in wind power supply must be compensated byancillary energy sources which can include the use of fossil fuels. Improvedprediction of wind power will help to reduce dependency on supplemental energysources along with their associated costs and emissions. In this paper, wediscuss limitations of current predictive practices and explore the use ofMachine Learning methods to enhance wind ramp event classification andprediction. We additionally outline a design for a novel approach to wind rampprediction, in which high-resolution wind fields are incorporated to themodelling of wind power.

Integrating wind variability to modelling wind-ramp events using a non-binary ramp function and deep learning models