at Google ScholarTitle data
Fu, Kun ; Song, Ruihao ; Pant, Prashant ; Hamacher, Thomas ; Perić, Vedran S.:
A Self-adaptive Digital Twin with Broad Learning System : an Example of Heat Pump.
In:
2024 IEEE PES Innovative Smart Grid Technologies Europe (ISGT EUROPE). -
Dubrovnik, Croatia
,
2024
DOI: https://doi.org/10.1109/ISGTEUROPE62998.2024.10863682
Abstract in another language
This paper introduces a novel self-adaptive digital twin (DT) based on broad learning system (BLS), which has potential to be evolved in the power and energy sectors. Traditional data-driven DT approaches in these sectors struggle with the requirement for extensive historical data and flexibility in adapting to changes in operating conditions. By integrating BLS, our method notably decreases the volume of initial training data required and improves the system’s ability to adjust to new conditions uncovered in initial training data. As an example, the proposed method is applied on a 5 kW air-source heat pump system. Finally, the effectiveness of the proposed method is demonstrated through comparison with a benchmark model calibrated with experimental data.
Further data
| Item Type: | Article in a book |
|---|---|
| Refereed: | No |
| Institutions of the University: | Faculties > Faculty of Engineering Science > Chair Intelligent Energy Management > Chair Intelligent Energy Management - Univ.-Prof. Dr. Vedran Peric |
| Result of work at the UBT: | No |
| DDC Subjects: | 600 Technology, medicine, applied sciences > 620 Engineering |
| Date Deposited: | 26 Mar 2026 10:17 |
| Last Modified: | 26 Mar 2026 10:17 |
| URI: | https://eref.uni-bayreuth.de/id/eprint/96184 |