Data-driven predictive control of buildings; a regression based approach

Publikationstyp
Conference Paper
Date Issued
2019-08
Sprache
English
Author(s)
Khosravi, Mohammad  
Eichler, Annika  
Aboudonia, Ahmed  
Buck, Roger  
Smith, Roy S.  
TORE-URI
http://hdl.handle.net/11420/12757
Start Page
605
End Page
610
Article Number
8920573
Citation
CCTA 2019 - 3rd IEEE Conference on Control Technology and Applications: 8920573, 605-610 (2019-08)
Contribution to Conference
3rd IEEE Conference on Control Technology and Applications, CCTA 2019  
Publisher DOI
10.1109/CCTA.2019.8920573
Scopus ID
2-s2.0-85077800189
Publisher
IEEE
ISBN of container
978-1-7281-2767-5
978-1-7281-2768-2
In this paper, we present a data-driven predictive control (DDPC) strategy suitable for (general bilinear) building energy systems, which only relies on historical measurements. The introduced control technique operates iteratively in a receding horizon scheme. During the operation of DDPC, using available historical building data which are collected during the normal operation of building, the dynamics of building are approximated and estimated. Therefore, the system dynamics are decomposed into two stable subsystems, one describing the thermal dynamics of the mass of building and the other one describing the dynamics of the temperature of rooms leading to a linear infinite-dimensional interconnected system. Linear regression is used to estimate a finite approximation thereof. Employing the approximate model, a cost is minimized in each iteration. The performance of the introduced data-driven control method is numerically verified for a validated building simulation environment.
DDC Class
004: Informatik
More Funding Information
This research project is part of the Swiss Competence Center for Energy Research SCCER FEEB&D of the Swiss Innovation Agency Innosuisse.