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the actual European energy context highlights the building sector as one of the largest sectors of energy consumption. Consequently, the "Energy Performance of Buildings Directive", adopted in 2002 and focusing on energy use in buildings, requires all the EU members to enhance their building regulations and to introduce energy certification schemes, with the aim of both reducing energy consumption and improving energy efficiency. That is why carrying out an energy performance diagnosis is mandatory, notably when buying or selling properties. Indeed, invisible defaults, like, for example, non-emerging cracks or delaminations, could have a detrimental effect on insulating qualities. Esimaing in-situ thermo-physical properties allowing locating these defaults, the present work focuses on proposing new and efficient approaches based on the use of both artificial intelligence tools (artificial neural networks and neuro-fuzzy systems) and inverse methods for characterizing building materials i.e. for estimating their thermal diffusivity using thermograms obtained thanks to a non-destructive photothermal method.

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As part of the second phase of the OptiEnR research project, the present work deals with improving the operation of a multi-energy district boiler by adding to the plant an optimally designed and controlled thermal storage tank. Previous study focused on both a design approach, based on a parametric analysis, and a non-predictive control strategy. The aim of the present work was to develop a Model Predictive Controller (MPC) to improve the management of the tank in real time. The proposed controller generates optimal command sequences dealing with the amount of thermal energy to be stored or released. As a result, both the fossil energy consumption and CO 2 emissions are significantly reduced while the economic gain is increased.

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