Computational science

Research

Wind energy systems, reanalysis bias correction, and uncertainty quantification, integrated into continental-scale energy system optimisation with an emphasis on reproducible research.

Completed

PhD - Data Science-Enhanced Wind Power Modelling: From Reanalysis Correction to Energy System Representation

A comprehensive research project for improving wind power modelling through data-driven bias correction and enhanced representation in energy system models.

Published

Geographic variability in reanalysis wind speed biases: A high-resolution bias correction approach for UK wind energy

A spatially granular bias correction method for improving wind resource estimates and energy system planning outcomes.

Open
Published

Improving wind power modelling through granular spatial and temporal bias correction of reanalysis data

A novel bias correction method for enhancing the accuracy of wind resource assessments and energy system optimization.

Open
Published

Multi-Output Regression with Generative Adversarial Networks (MOR-GANs)

A novel approach for multi-output regression using generative adversarial networks, enhancing predictive performance in complex systems.

Open