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Building behaviour identification based on on-board monitored data

This PhD-project is part of a funded research project on ‘Building performance characterization and assessment based on in-situ measurements’. The project starts from the idea that decreasing the energy use in buildings can only be achieved by an accurate characterization of the as built energy performance of buildings. This is mainly for two reasons. First of all, despite the ever more stringent energy legislation for new and renovated buildings, monitoring the actual energy performances reveals in many cases a significant performance gap with the theoretically designed targets. Secondly, the ever increasing need for integration of renewables stresses the existing energy systems which can be remedied by using intelligent grids that are aware of the actual status of the buildings in a district. A reliable characterization and assessment of the actual performance of buildings can only be realized by optimized in-situ measurements combined with statistical dynamic data analysis techniques. The part of the project you will be focusing on is the building behavior characterization. The main goal of the PhD is to develop and optimize system identification methods that can be used in model predictive control, fault detection, optimization of district systems, …, The methods should be able to translate the dynamic behavior of a building as monitored during real life use into a simplified model that can be applied to optimize and reduce the final energy use of a building or a building district. The research will be based on past and current research at the Building Physics Section. Furthermore, the position is embedded in the Annex 71-project of the Energy in Buildings and Communities program of the International Energy Agency (www.iea-ebc.org/projects/project?Annex71)on “Building Energy Performance Assessment based on in-situ measurements”.

Date:10 Dec 2020  →  Today
Keywords:Building Stock Behavior Identification
Disciplines:Energy in buildings and built environments
Project type:PhD project