Abstract
This paper describes the developed digital twin of an office building and data centre complex. The definition of how a digital twin used here is that it looks the same and behaves in the same way as the real counterpart. This is implemented with integration of the Building Information Model (BIM), energy measurements and predictions of energy consumptions of the building with neural networks. A 3D viewer is used to enable the visual analysis of the energy consumption of different parts of the building.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 37th Conference on Design of Circuits and Integrated Circuits, DCIS 2022 |
| Publisher | IEEE Institute of Electrical and Electronic Engineers |
| Pages | 1-5 |
| Number of pages | 5 |
| ISBN (Electronic) | 978-1-6654-5950-1 |
| DOIs | |
| Publication status | Published - 8 Dec 2022 |
| MoE publication type | A4 Article in a conference publication |
| Event | 37th Conference on Design of Circuits and Integrated Circuits, DCIS 2022 - Pamplona, Spain Duration: 16 Nov 2022 → 18 Nov 2022 |
Conference
| Conference | 37th Conference on Design of Circuits and Integrated Circuits, DCIS 2022 |
|---|---|
| Country/Territory | Spain |
| City | Pamplona |
| Period | 16/11/22 → 18/11/22 |
Funding
The work behind this article have been made possible by the EU Stardust Smart City project, which has received funding from the European Union s Horizon 2020 research and Innovation programme under grant agreement N 774094 and CityIoT (Future operator independent data integration platform), which is funded by European Regional Development Fund.
Keywords
- ANN model
- BIM
- BIM model server
- data centre
- data platform
- deep learning
- digital twin
- energy consumption
- energy data transfer
- machine learning
- prosumer
- Stardust
- virtual model
