Role
I am a Lecturer in Electrical and Electronic Engineering (EEE). My teaching focuses on renewable energy technologies, smart grids, and the application of artificial intelligence in energy systems, reflecting the latest technological and industrial developments. The content of my modules highlights the global transition towards sustainable and low-carbon energy systems, providing students with both the technical and contextual understanding needed to address modern energy challenges.
A key aspect of my work is the transfer of knowledge in emerging green technologies, helping to bridge the growing gap between technological innovation and the human skills required to deploy, operate, and manage these systems effectively. As new technologies rapidly advance, developing the next generation of skilled professionals becomes essential to achieving a just and sustainable energy transition.
I adopt innovative, student-centred teaching methods that promote active engagement, independent thinking, and problem-based learning. By integrating real-world case studies and research-led material, I aim to equip students with the analytical and practical skills required to contribute effectively to a more sustainable and digitally connected energy future.
Career overview
Before joining NTU, I contributed to several research initiatives at the intersection of data-driven modelling, sustainable energy, and local energy markets. My work applies advanced machine learning and deep learning techniques to support the energy transition and the integration of low-carbon technologies.
I have been involved in major collaborative projects including EnergyREV, which explored digitalisation and flexibility within smart local energy systems, and Horizon Europe Drive2X, focused on developing intelligent frameworks for Vehicle-to-Everything (V2X) energy exchange. In addition, through Impact Acceleration Account (IAA) projects, I have collaborated with several UK local authorities, including Perth and Kinross, Edinburgh, and the Scottish Borders councils, to support data-driven approaches to sustainable energy planning and policy innovation.
My research has encompassed the development of deep learning models for V2X flexibility forecasting, location-based neural networks for electric vehicle charging demand prediction, and behavioural models that capture and explain customer engagement with smart green technologies, alongside the creation of an open-source platform for local energy market simulation and modelling.
Research areas
My research focuses on the application of advanced computational and data-driven methods to energy and mobility systems, with the goal of supporting a fair, transparent, and efficient energy transition.
Key areas of interest include:
- Leveraging data-driven techniques and AI to solve timely challenges in the transition to net zero, including decentralised coordination of local energy systems and advanced forecasting of distributed energy flexibility.
- Local energy markets and decentralised optimisation,
- Behavioural and explainable modelling, to understand, predict, and interpret customer engagement and adoption of smart green technologies.
Sustainable energy planning, linking digital technologies with policy and decision-support tools to promote equitable access to clean energy.
Publications
For a full list of recent publications and research outputs, please refer to my Google Scholar Profile