Empirical validation of the thermal model of a passive solar cell test

TL;DR

This paper empirically validates a thermal model of a passive solar building, emphasizing sensitivity analysis and input-error correlation to enhance model accuracy through frequency domain techniques.

Contribution

It introduces a frequency domain sensitivity analysis method and a two-sample validation approach for improving thermal building models.

Findings

Frequency domain sensitivity analysis identifies key parameters.

Time-frequency analysis helps locate sources of model error.

Validated model shows improved accuracy in predicting indoor temperature.

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.