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Highlights • An inverse heat transfer model linked to Bayesian statistics is presented. • This model is used to identify the conductivity and volumetric heat capacity. • In situ measurements are used by the inverse model. • The proposed model can use shorter time series than the standard ISO 9869. • The proposed model produces more accurate and robust results compared to the standard. Abstract Non-destructive thermal diagnosis is necessary for identification and quantification of structural defects and verification of construction performances. In this paper, we test different inverse heat transfer models to identify the thermal conductivity of a building's wall in its environment. The approaches proposed and used in this paper rely only on non-intrusive measurements: inside and outside wall surface temperatures and heat flow through the wall. First, a Bayesian statistical dynamic inference method, which has the advantage to quantify the unknown parameter and its credible interval, is presented. This method considers the uncertainties of the measured temperature and heat flow data and of the unknown thermal properties. Markov chain Monte Carlo (MCMC) algorithm is used to explore the posterior distribution. Then, the average and the dynamic procedures ISO 9869 (I. 9869-1, 2014) are introduced. Finally, the probabilistic distributions of the unknown parameters are presented and compared to the standard results. The impact of experimental conditions (average indoor-to-outdoor temperature) and the measurements length on the accuracy of the results are discussed. The relationship between the number of iterations of the MCMC , time series length, shape of the prior distribution and accuracy are studied as well as the simulation time to run the inverse models. The Bayesian approach gives the most accurate results and has the advantage of considering several unknowns (conductivity and volumetric heat capacity), which is not the case for the studied standard. The Bayesian method needs much shorter time series than the ISO standard and produces robust results at all times of year, including when the average indoor-to-outdoor temperature difference was low. [ABSTRACT FROM AUTHOR]

A building influences its surrounding external environment, and its indoor environment is usually sensitive to its surroundings. In order to obtain a more accurate prediction of the indoor thermal behaviour of a building or a building stock, it is necessary to consider the interactions between the indoor and outdoor thermal environments. The modelling approach presented in this paper is based on a building energy model and the urban modelling tool SOLENE-Microclimat. A sensitivity analysis is undertaken to highlight the parameters which influence the performance of the building model. Then, the thermal indoor behaviour of a building is compared to in situ experimental measurements and the outdoor thermal environment is evaluated in terms of external surface temperatures. The model's accuracy and behaviour is evaluated and a crossover approach with the sensitivity analysis results is proposed. The model obtains a good level of performance for almost all variables, unless some external surface temperatures intervals. [ABSTRACT FROM AUTHOR]

Innovative and efficient ways to perform numerical simulations are worth investigations to reduce the computational complexity of building models and permit to solve complex problems. This paper proposes a Proper Generalised Decomposition (PGD) model reduction technique for heat and moisture multizone modelling. First, the transient multizone problem is written using PGD formalism and solved in a non-incremental way to assess hygrothermal fields. The computational complexity and accuracy of results of the reduced-order model and the large original model are compared, highlighting that the fields are precisely represented with the PGD resolution. The complexity of the reduced-order model is lower, offering interesting numerical savings, particularly when the number of the building zones increases. The second advantage of the method is for solving parametric problems. Two illustrations are proposed. The first considers a climate and ventilation flow rates parametric multizone problem. The PGD model analyse the behaviour of building zones as a function of these two extra-parameters. For the second, an initial condition and source-term PGD parametric solution is computed. It is then coupled with a wall model to perform whole-building hygrothermal simulation. For both studies, the model represents accurately the hygrothermal fields in the different zones with important computational savings. [ABSTRACT FROM AUTHOR]

Innovative and efficient ways to carry out numerical simulations are worth of investigation to reduce the computational complexity of building models and make it possible to solve complex problems. This paper presents a reduced order model, based on Proper Generalised Decomposition (PGD), to assess 2-dimensional heat and moisture transfer in walls. This model is associated with the multizone model Domus using an indirect coupling method. Both models are co-simulated to perform whole-building hygrothermal simulation, considering 2D transfer in walls. The whole-building model is first validated with data from the IEA Annex 41. Then, a case study is considered taking into account a 2-zones building with an intermediary shared wall modelled in 2 dimensions to illustrate the importance of the technique to analyse the hygrothermal behaviour of the wall. It has been highlighted that the whole model enables to perform more precisely analyses such as mould growth on the internal surface. In addition, important theoretical numerical savings (90%) are observed when compared to the large original model. However, the effective numerical savings are not so important (40%) due to the limitations of the co-simulation method. [ABSTRACT FROM AUTHOR]

The urban context is often simplified or neglected in Building Energy Models (BEMs) due to the difficulties of taking accurately into account all the heat fluxes emanating from the environment. Oversimplifying the urban context can impact the accuracy of the BEM predictions. Nevertheless, several approaches can be used to allow for the impact of the urban environment on the dynamic behavior of a building, its heating and cooling demands, and thermal comfort. This state of the art review provides a critical overview of the different methods currently used to take into account the urban microclimate in building design simulations. First, both the microclimate and building models are presented, focusing on their assumptions and capabilities. Second, a few examples of coupling, performed between both modeling scales are analyzed. Last, the discussion highlights the differences obtained between simulations that take the urban context into consideration and those that simplify or neglect urban heat fluxes. The remaining scientific obstacles to a more effective consideration of the urban context impacting the BEMs are indicated. • The local data produced by a selection of urban climate models (UCMs) are presented. • An analysis of how the urban context is taken into account in building energy models (BEMs) is given. • Several chaining or coupling strategies to link UCMs and BEMs are analyzed and compared. • Using local climate data leads to a noticeable impact on the thermal behavior of buildings. • Recommendations for better consideration of the urban context in BEMs are formulated. [ABSTRACT FROM AUTHOR]