Here are the research projects I have coordinated, or am still coordinating.
BAYREB: Bayesian inference for building energy performance assessment
The BAYREB project belongs to the general field of the energy refurbishment of buildings. It aims at providing decision makers and renovation experts with decision support tools for the renovation process regarding energy efficiency. The project is part of a workflow based on stochastic methods that will support the decision process in a twofold manner:
- Using in situ sensor measurements, aided with Bayesian inference and a prior model of the building, to evaluate its real energy performance, diagnose envelope properties and eventual pathologies, while providing confidence intervals for all inferred data;
- Using the acquired knowledge of the true state of the building, and its uncertainty, as a basis for the elaboration of optimal renovation solutions.
All results of the project have been summarised in a single report available here. More detail regarding specific points have been published in papers, referred below.
MODERNAT: Data-driven modelling of coupled heat and air transfer for the estimation of natural cooling potentials
The demand for air conditioning of buildings is bound to increase in the next decades, due to the combined effects of climate change and increasing comfort standards in all countries. The need to reduce the energy demand for cooling has led practitioners to reinstate and adapt traditional natural cooling practices, some of which have existed for centuries: ventilative cooling, cool roofs, green roofs, evaporative cooling, etc.
The main barrier to a successful generalisation of natural cooling is the difficulty to ensure its performance. Accurate predictions of air flow rates are a necessary condition for a reliable design, but are also very uncertain. Moreover, once a natural cooling solution has been implemented, there is no way of assessing its effectiveness on thermal comfort.
In order to address these challenges, we propose the development of data-driven modelling of coupled heat and air transfer in buildings, especially in warm weather conditions. The strategy of the project lays on using statistical learning methods for the characterisation and prediction of heat and air flow.
This project just started in July 2020: I haven’t put up a website yet