Patrick Schalbart

Empirical validation of different internal superficial heat transfer models on a full-scale passive house

Being highly insulated, low-energy buildings are very sensitive to variable solar and internal gains. In this context, some modelling assumptions frequently used in simplified building energy simulation tools might be called into question. While higher insulation levels reduce the influence of heat transmission through opaque walls, absorption of solar and internal gains at inner wall surfaces, and indoor superficial heat transfers, become concerning. The convective and long-wave radiative heat transfer models are investigated in COMFIE, a dynamic energy simulation platform. More detailed internal heat transfer models are developed by decoupling convective and long-wave radiative heat transfers and using time-dependent coefficients. Furthermore, an empirical validation process on both simplified and detailed models is carried out using measurements from a full-scale experimental concrete passive house, addressing the model uncertainty vs. complexity issue.

Derivation of simplified control rules from an optimal strategy for electric heating in a residential building

In France, 40% of buildings are heated with electrical devices causing high peak load in winter. In this context, advanced control systems could improve buildings energy management. More specifically, optimal strategies have been developed using a dynamic programming method in order to shift heating load, taking advantage of the building thermal mass. However, this optimization method is computationally intensive and can hardly be applied to real-time control. Statistical techniques can be used to derive near-optimal laws from the optimal control results. These rule extraction techniques model the relationship between explanatory variables and a response variable. This paper investigates the use of Beta regression model. This regression-based strategy was able to mimic the general characteristics of the optimization results with a small mean bias error (−6%) and greatly reduce computational effort (150 times faster). Given its simple mathematical formulation, it could be implemented in real-time building systems control.