Inverse modeling of simplified hygrothermal building models to predict and characterize indoor climates

Computational research on monumental buildings yields three problems regarding currently used detailed building models: tedious modeling, relatively long simulation run times, difficulties to characterize a building by its model parameters. A new simplified hygrothermal building model in state space form is presented with an inverse modeling technique to identify its parameters. Based on a literature review, 10 thermal models and 5 hygric models were developed. An optimization routine was used to fit the output of the models to long term hourly measurements of a typical monumental building zone and to a fictive indoor climate that was simulated by a validated simulation tool. The model performance was assessed by three criteria and the best models were selected. The validation of the selected thermal and hygric models consisted of fitting the models’ output to indoor climate measurements of four monumental building zones, a residual analysis and parameter analysis. The results show that the simplified hygrothermal model is capable of reproducing most indoor climates accurately. Moreover, the state space model results in fast simulations: 100 years with hourly samples was simulated in 0.45 sec on an ordinary computer (i5-processor). Characterization and validation of the parameter values is challenging and requires additional measurements and research