A multi-objective optimization strategy to reduce correlation and uncertainty for thermal response test analysis

Abstract

The analysis of thermal response tests by optimization-based methods can lead to highly correlated parameters and to a relatively uncertain identification of the thermal parameters. The multi-objective optimization strategy examined here combines the common root mean square error to describe the misfit between the measured and simulated fluid temperature with the root mean square error between the temperature derivative of the same variables. The final objective function combines also the partial correlation coefficients of the temperature and derivative signals. The four objective functions are combined by scalarizing the multi-objective problem. The method proposed here was tested using three real data sets produced by the thermal response tests performed on a helical shaped pipe and on two single U-bent ground heat exchangers. The multi-objective strategy did not lead to different thermal parameters for the three tests, but it did generate a significant reduction in the correlation and uncertainty of the parameters. In addition, a more compact optimum shape can lead to a more rapid convergence time.