at Google ScholarTitle data
Zinsmeister, Daniel ; Ludolfinger, Ulrich ; Perić, Vedran S. ; Goebel, Christoph:
A benchmarking framework for energy management systems with commercial hardware models.
In: Energy and Buildings.
Vol. 321
(2024)
.
- 114648.
ISSN 0378-7788
DOI: https://doi.org/10.1016/j.enbuild.2024.114648
Abstract in another language
Energy Management Systems (EMS) for buildings are pivotal in leveraging flexibility from sector coupling in future power systems. Currently, most EMS are designed and evaluated using non-standardized and incompatible simulation models built within the specific EMS development cycle. Such evaluation techniques make it difficult to compare different EMS solutions according to well-defined and universal performance indicators. Therefore, open-access benchmark models of realistic building energy systems would be beneficial for wider research community. This article introduces the ProHMo benchmarking framework which provides experimentally validated commercial heating and cooling equipment models in various building energy system configurations. The building energy system models are available as openly accessible Functional Mock-Up Units (FMU) to allow for toolchain-independent benchmarking of EMS. The framework includes a Python code template that enables easy integration with different EMS interfaces. In a case study, we show the potential of the benchmarking framework by comparing a rule-based, optimization-based, and reinforcement learning-based EMS. The results show that the optimization-based EMS with perfect foresight achieves the lowest costs. Although the reinforcement learning-based EMS performs slightly poorer, it operates independently of forecasts, which makes it attractive for practical applications. The ProHMo benchmarking framework is designed to equip researchers with a robust framework for developing, evaluating, and comparing different EMS, particularly those focused on optimization and data-driven control methods.
Further data
| Item Type: | Article in a journal |
|---|---|
| Refereed: | Yes |
| Keywords: | Modelica; FMI; Open access; Energy management system; Model predictive control; Reinforcement learning; Experimental validation |
| 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: | 25 Mar 2026 08:24 |
| Last Modified: | 25 Mar 2026 08:24 |
| URI: | https://eref.uni-bayreuth.de/id/eprint/96178 |