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

Sharp, Russell; Ihshaish, Hisham; Deza, Juan Ignacio

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.

• Presentation Conference Type: Conference Paper (unpublished)
• Conference Name: International Conference for Sustainable Ecological Engineering Design for Society, SEEDS 22
• Start Date: Aug 31, 2022
• End Date: Sep 2, 2022
• Deposit Date: Sep 5, 2022
• Keywords: Wind modelling, wind variability, wind-ramp events, non-binary ramp function, deep learning, deep learning models
• Public URL: https://uwe-repository.worktribe.com/output/9948345
• Related Public URLs: https://www.leedsbeckett.ac.uk/events/conferences/seeds-conference-2022/