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Research interests

I am interested in computational problem-solving across the domains of buildings and energy systems. This spans improvements to models and simulations, using optimisation approaches like genetic algorithms to explore the space of possible designs, and cutting-edge machine intelligence techniques. My work bridges the building, district and city scales, and is inspired by the principles of systems thinking regarding holistic analysis and interconnectivity. I am also interested in the process of software development in an academic context, and in improving the exchange of knowledge with commercial partners.

I retain an affiliation with my previous group, the  at Empa and ETH Zurich in Switzerland, where I continue to supervise students.

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Building and Energy System Simulation

Using simulation to understand the behaviour of buildings and energy systems.

Future holistic, integrated energy systems solutions span from buildings (which are now active players in energy markets) to district and city-level designs and national scale infrastructure. Simulation is the sole means of exploring the performance of new designs and concepts in a rapidly changing techno-economic context; all buildings and systems are unique (there are no prototypes) and simulation can explore the influence of future contextual parameters (energy prices, warmer climates, upgraded technologies) on new designs.

Computational Optimisation

Exploring design spaces by examining trade-offs between multiple objectives.

The design space of possible systems and their performance is vast and intricate, making it impossible to discover the best solutions by trial-and-error or by exhaustive evaluation of all options. Computational design optimization (e.g. multi-objective genetic algorithms and mixed-integer linear programming formulations) are powerful tools for finding high-performing designs and exploring their sensitivity, robustness and resilience.

Machine Intelligence

Leveraging developments in machine learning to better explore systems and data.

The next steps in achieving real progress in computational design aids will encompass statistical emulation of complex models and hyper-heuristic optimisers that learn how to solve problems better. Statistical emulators (also called meta-modelling) use methods like neural networks to approximate detailed models that are too time-consuming to run directly. Hyper-heuristics involves “optimising the optimiser”, either in advance using training data or during an optimisation