Uncertainty Analysis of a Hardware in Loop Setup for Testing Products Related to Building Technology

Authors: Balasundaram Prasaant, Ploix Stephane, Delinchant Benoit, Muresan Cristian

Abstract:

Hardware in Loop (HIL) testing is done to test and validate a particular product especially in building technology. When it comes to building technology, it is more important to test the products for their efficiency. The test rig in the HIL simulator may contribute to some uncertainties on measured efficiency. The uncertainties include physical uncertainties and scenario-based uncertainties. In this paper, a simple uncertainty analysis framework for an HIL setup is shown considering only the physical uncertainties. The entire modeling of the HIL setup is done in Dymola. The uncertain sources are considered based on available knowledge of the components and also on expert knowledge. For the propagation of uncertainty, Monte Carlo Simulation is used since it is the most reliable and easy to use. In this article it is shown how an HIL setup can be modeled and how uncertainty propagation can be performed on it. Such an approach is not common in building energy analysis.

Keywords: Energy in Buildings, Hardware in Loop, Modelica (Dymola), Monte Carlo Simulation, Uncertainty Propagation.

References:

[1] Mehrfeld, Philipp, Markus Nürenberg, Martin Knorr, Lars Schinke, Maximilian Beyer, Manuel Grimm, Moritz Lauster, Dirk Müller, Joachim Seifert, and Konstantinos Stergiaropoulos, “Dynamic evaluations of heat pump and micro combined heat and power systems using the hardware-in-the-loop approach” ,Journal of Building Engineering 28 (2020): 101032.
[2] Schütz, Thomas, Markus Hans Schraven, Marcus Fuchs, Peter Remmen, and Dirk Müller. "Comparison of clustering algorithms for the selection of typical demand days for energy system synthesis." Renewable energy 129 (2018): 570-582.
[3] Abbiati, Giuseppe, Stefano Marelli, O. S. Bursi, Bruno Sudret, and Bozidar Stojadinovic. "Uncertainty propagation and global sensitivity analysis in hybrid simulation using polynomial chaos expansion." In Proc. 4th Int. Conf. Soft Comput. Tech. Civil, Struct. Environ. Eng. Prag (Czech Republic). 2015.
[4] Tian, Wei, Yeonsook Heo, Pieter De Wilde, Zhanyong Li, Da Yan, Cheol Soo Park, Xiaohang Feng, and Godfried Augenbroe. "A review of uncertainty analysis in building energy assessment." Renewable and Sustainable Energy Reviews 93 (2018): 285-301.
[5] Ghanem, Roger, David Higdon, and Houman Owhadi, eds. Handbook of uncertainty quantification. Vol. 6. New York: Springer, 2017.
[6] Landau, David P., and Kurt Binder. A guide to Monte Carlo simulations in statistical physics. Cambridge university press, 2014.