Systematic model selection for grey box modeling of HVAC systems
- Grey Box models provide an important approach for control analysis in the Heating, Ventilation and Air Conditioning (HVAC) sector. Grey Box models consist of physical models where parameters are estimated from data. Due to the vast amount of component models that can be found in literature, the question arises, which component models perform best on a given system or dataset? This question is investigated systematically using a test case system with real operational data. The test case system consists of a HVAC system containing an energy recovery unit (ER), a heating coil (HC) and a cooling coil (CC). For each component, several suitable model variants from the literature are adapted appropriately and implemented. Four model variants are implemented for the ER and five model variants each for the HC and CC. Further, three global optimization algorithms and four local optimization algorithms to solve the nonlinear least squares system identification are implemented, leading to a total of 700 combinations. The comparison of all variants shows that the global optimization algorithms do not provide significantly better solutions. Their runtimes are significantly higher. Analysis of the models shows a dependency of the model accuracy on the number of total parameters.
Author: | Valentin Seiler, Gerhard Huber, Peter KepplingerORCiD |
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DOI: | https://doi.org/10.5281/zenodo.10245218 |
ISBN: | 978-1-912669-63-9 |
Parent Title (English): | 10th Heat Powered Cycles Conference Proceedings. 3-6 September, 2023. Edinburgh, Scotland, UK. |
Publisher: | The University of Edinburgh |
Place of publication: | Edinburgh, Scotland |
Editor: | Roger Riehl, Giulio Santori, Markus Preißinger |
Document Type: | Conference Proceeding |
Language: | English |
Year of publication: | 2023 |
Release Date: | 2024/01/23 |
Tag: | Grey Box; HVAC; Model selection; Modeling |
Number of pages: | 15 |
First Page: | 162 |
Last Page: | 176 |
Organisationseinheit: | Forschung |
Forschung / Forschungszentrum Energie | |
Forschung / Josef Ressel Zentrum für Intelligente Thermische Energiesysteme | |
DDC classes: | 600 Technik, Medizin, angewandte Wissenschaften |
Open Access?: | ja |
Peer review: | wiss. Beitrag, peer-reviewed |
Publicationlist: | Huber, Gerhard |
Kepplinger, Peter | |
Preißinger, Markus | |
Seiler, Valentin | |
Licence (German): | Creative Commons - CC BY - International - Attribution- Namensnennung 4.0 |