Chair in Sustainable Engineering

Integrating a higher share of renewables into the energy mix requires adapting electricity grids to demand, as renewable energy cannot be stored at scale, while energy flows can be adjusted to fluctuations. Fixed consumption models are well suited to fossil-fuel-based systems, but renewable-based systems require realistic, dynamic models of production and consumption.

The goal is to develop models applicable to energy systems that balance the complexity of real-time control with the need to meet fluctuating energy demand. By integrating demand-side data, these models enable effective demand allocation and the adaptation of power generation to variable renewable supply.

The chair explores new approaches to energy modelling by fully integrating the demand side and combining technical aspects with social and behavioural factors. This vision aligns with the transdisciplinary ambition of the Swifft Collective Chairs, fostering collaboration with other chairs on issues such as social acceptance, institutional trust, and broader behavioural patterns.

Using machine learning as a foundational tool, the chair develops predictive models and software for Brussels’ energy system, producing scenarios of optimal energy mixes while incorporating internal and external sources of uncertainty, leading to more robust and interpretable models. By bridging the gap between academia and policy through usable, maintainable, and open frameworks, the chair supports a transition that remains just and democratic, grounded in tools that are transparent, trustworthy, and accessible.