Parameter identification of a Round-Robin test box model using a deterministic and probabilistic methodology

摘要

In this paper, two different methodologies are applied to the parameter estimation problem of a computational model of a Round-Robin test box. The numerical model is developed using TRNSYS. A global sensitivity analysis is carried out to determine the most important parameters that should be considered in the subsequent calibration procedure. Using the Bayesian (probabilistic) approach, the posterior distribution of the unknown input parameters is estimated via simulation techniques. Using the deterministic approach, executed in GenOpt, the calibration is performed by the minimization of an objective function that measures the differences between model predictions and real measured data. Parameter estimation results obtained with both methodologies are then compared and discussed. A reduction of the Coefficient of Variation of the Root Mean Square Error (CV (RMSE)) after calibration over 40% with both methods has been obtained, being the CV (RMSE) for calibration and validation periods on average 3.21% and 2.40%, respectively.