Parametric sensitivity analysis of a test cell thermal model using spectral analysis

TL;DR

This paper presents a spectral analysis-based sensitivity method for validating and improving a building thermal model by identifying key parameters and input errors through frequency domain analysis.

Contribution

It introduces a frequency domain sensitivity analysis technique for building thermal models, enhancing model calibration and validation processes.

Findings

Identified key parameters affecting model accuracy.

Demonstrated the effectiveness of time-frequency analysis in error source identification.

Validated the model with experimental data.

Abstract

The paper deals with an empirical validation of a building thermal model. We put the emphasis on sensitivity analysis and on research of inputs/residual correlation to improve our model. In this article, we apply a sensitivity analysis technique in the frequency domain to point out the more important parameters of the model. Then, we compare measured and predicted data of indoor dry-air temperature. When the model is not accurate enough, recourse to time-frequency analysis is of great help to identify the inputs responsible for the major part of error. In our approach, two samples of experimental data are required. The first one is used to calibrate our model the second one to really validate the optimized model.