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

TL;DR

This paper proposes a novel deep learning approach that incorporates high-resolution wind data to improve the prediction of wind ramp events, aiming to enhance wind energy integration and reduce reliance on fossil fuels.

Contribution

It introduces a new modeling framework combining non-binary ramp functions with deep learning and high-resolution wind data for better wind ramp event prediction.

Findings

Improved accuracy in wind ramp event classification.

Enhanced wind power forecasting using high-resolution wind fields.

Potential reduction in reliance on fossil fuel-based backup energy.

Abstract

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.