Szczegóły publikacji
Opis bibliograficzny
Digital twin-based simulation of smart building energy performance: BIM-integrated MATLAB/Simulink framework for BACS and SRI evaluation / Gabriela WALCZYK, Andrzej OŻADOWICZ // Energies [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 1996-1073 . — 2026 — vol. 19 iss. 2 art. no. 543, s. 1–37. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 36–37, Abstr. — Publikacja dostępna online od: 2026-01-21
Autorzy (2)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 165860 |
|---|---|
| Data dodania do BaDAP | 2026-02-20 |
| Tekst źródłowy | URL |
| DOI | 10.3390/en19020543 |
| Rok publikacji | 2026 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Energies |
Abstract
The increasing role of automation systems in energy-efficient buildings creates a need for simulation approaches that support standardized assessment already at the design stage. This paper presents a digital twin-based simulation framework that integrates building information modeling (BIM)-derived building data with MATLAB/Simulink models to enable regulation-oriented evaluation of building automation and control strategies. The proposed approach targets scenario-based analysis of automation maturity levels, covering conventional, advanced, and predictive configurations aligned with EN ISO 52120 and the Smart Readiness Indicator (SRI). A representative academic building model is used to demonstrate how the framework supports reproducible modeling of heating, ventilation, and air conditioning (HVAC), lighting, and shading control functions and enables consistent comparison of their energy-related behavior under unified boundary conditions. The results show that the framework effectively captures performance trends associated with increasing automation sophistication and reveals interaction effects between control subsystems that are not accessible in conventional energy simulation tools. The proposed methodology provides a practical and extensible foundation for early-stage, regulation-aligned evaluation of smart building solutions and for the further development of predictive and artificial intelligence (AI)-assisted control concepts.
