Building Thermal-Network Models: A Comparative Analysis, Recommendations, and Perspectives

Abhinandana Boodi, Karim Beddiar, Yassine Amirat and Mohamed Benbouzid
Additional contact information
Abhinandana Boodi: CESI Brest Campus, EA 7527 LINEACT, 29200 Brest, France
Karim Beddiar: CESI Brest Campus, EA 7527 LINEACT, 29200 Brest, France
Yassine Amirat: ISEN Yncréa Ouest, L@bISEN, 29200 Brest, France
Mohamed Benbouzid: Institut de Recherche Dupuy de Lôme (UMR CNRS 6027 IRDL), University of Brest, 29238 Brest, France

Energies, 2022, vol. 15, issue 4, 1-27

Abstract: The development of smart buildings, as well as the great need for energy demand reduction, has renewed interest in building energy demand prediction. Intelligent controllers are a solution for optimizing building energy consumption while maintaining indoor comfort. The controller efficiency on the other hand, is mainly determined by the prediction of thermal behavior from building models. Due to the development complexity of the models, these intelligent controllers are not yet implemented on an industrial scale. There are primarily three types of building models studied in the literature: white-box, black-box, and gray-box. The gray-box models are found to be robust, efficient, of low cost computationally, and of moderate modeling complexity. Furthermore, there is no standard model configuration, development method, or operation conditions. These parameters have a significant influence on the model performance accuracy. This motivates the need for this review paper, in which we examined various gray-box models, their configurations, parametric identification techniques, and influential parameters.

Keywords: building energy; building energy management system (BEMS); gray-box models; lumped-parameter models; smart building; system identification; thermal-network models (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/4/1328/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/4/1328/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:4:p:1328-:d:747469

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().