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Data-Driven Predictive Control of Bilinear HVAC Dynamics - An Experimental Case Study
Publikationstyp
Journal Article
Date Issued
2024-12-17
Sprache
English
Author(s)
Journal
Volume
8
Start Page
3009
End Page
3014
Citation
IEEE Control Systems Letters 8: 3009-3014 (2024)
Publisher DOI
Scopus ID
Publisher
IEEE
Buildings are responsible for around 40% of the global energy demand. In order to effectively reduce the high energy consumption of HVAC systems while maintaining comfortable indoor climate, tailored control schemes are promising. Since the derivation of physical models of individual HVAC systems is time consuming, data-driven methods are a promising alternative. This paper proposes a framework for data-driven predictive control of HVAC system with bilinear system dynamics, which compensates for prediction errors via constraint adaptation through a bias term. The proposed scheme combines an extension of Willems' fundamental lemma to bilinear systems with the consideration of multiple data-sets. To evaluate the efficacy of the data-driven control scheme, an experimental case study is performed under realistic conditions. In comparison with an existing simple control scheme, our results demonstrate energy efficient operation and successful compensation of prediction errors.
Subjects
Bilinear systems | constraint adaptation | data-driven predictive control | Willems' fundamental lemma
DDC Class
600: Technology