No abstract

Due to the ill-posedness of many inverse problems, parameter estimates are often prone to a possibly large uncertainty, caused by a series of errors and approximations in the experimental and modelling work. Stochastic state-space models for time series modelling incorporate a term of process noise that represents system error; most studies on building thermal model calibration however employ deterministic models that overlook this error. This paper investigates how accounting for modelling errors affects the results of model calibration. Several simplified models are defined to simulate the indoor temperature of an experimental test cell. Some models include process noise and others do not. The parameters of each model are then learned repeatedly by using several training datasets from the test cell. The MCMC algorithm is used for training. The robustness of parameter estimates between independent trainings is evaluated. Then, the forecasting ability of the deterministic and stochastic options are compared, in terms of accuracy and robustness. Results show that stochastic modelling considerably increases the uncertainty of parameter estimates, but ensures their consistency between separate trainings, whereas deterministic models are less robust and offer a less reliable forecasting.

Ensuring the proper thermal performance of a building’s envelope upon reception is an important stage in the life cycle of the building. Several methods already exist for this purpose, and continue to be improved, such as co-heating, ISABELE, EPILOG, QUB and SEREINE. All these methods follow the common protocol consisting of heating the measured building. These measurement protocols quantify the dynamic evolution of interior and outdoor temperatures, and the thermal power injected into the building and these data are used in calibration algorithms to determine, by an inverse method to deduce a heat loss value. These methods require a difference of a few degrees between the interior and the exterior which can cause in summer periods a risk of damaging the building, as the outside temperature may already be high. The objective of this work is to explore the possibility of determining the intrinsic thermal performance of a building’s envelope in the summer period using a cooling system. This work leans on an experiment of a square meter scale cell and explore the capacities and limitations of the method at this scale by varying several stress parameters of the enclosure. Results in cooling mode are also compared to heating mode.