Details
| Original language | English |
|---|---|
| Title of host publication | EG-ICE 2021 Workshop on Intelligent Computing in Engineering, Proceedings |
| Editors | Jimmy Abualdenien, Andre Borrmann, Lucian-Constantin Ungureanu, Timo Hartmann |
| Pages | 34-43 |
| Number of pages | 10 |
| ISBN (electronic) | 9783798332126 |
| Publication status | Published - 2021 |
| Externally published | Yes |
| Event | 28th International Workshop on Intelligent Computing in Engineering of the European Group for Intelligent Computing in Engineering, EG-ICE 2021 - Virtual, Online Duration: 30 Jun 2021 → 2 Jul 2021 |
Publication series
| Name | EG-ICE 2021 Workshop on Intelligent Computing in Engineering, Proceedings |
|---|
Abstract
The building industry is benefited by building performance simulation (BPS) for design assistance. Machine learning (ML) has been widely used for quick performance prediction; however, it lacks the flexibility to scale for new designs. By spatially and semantically decomposing the building design into components, this article links the ML approach with the system engineering paradigm of BPS to develop component-based machine learning (CBML). While previous use of CMBL focused on point predictions, this study proves that the CBML is able to predict dynamic time-series energy performance for new design cases by deriving a set of reusable model components. We trained and tested the ML model on a dataset of 1000 examples. The objective is to ascertain the ability of the ML model to generalize via different decomposition levels. Hourly energy predictions during the design phase are useful for equipment sizing, controlling peak energy demands, and leveling the load in the networks.
ASJC Scopus subject areas
- Computer Science(all)
- Computer Science Applications
- Engineering(all)
- General Engineering
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
EG-ICE 2021 Workshop on Intelligent Computing in Engineering, Proceedings. ed. / Jimmy Abualdenien; Andre Borrmann; Lucian-Constantin Ungureanu; Timo Hartmann. 2021. p. 34-43 (EG-ICE 2021 Workshop on Intelligent Computing in Engineering, Proceedings).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Component-based machine learning for predicting representative time-series of energy performance in building design
AU - Chen, Xia
AU - Singh, Manav Mahan
AU - Geyer, Philipp
N1 - Funding information: We gratefully acknowledge the support of the German Research Foundation (DFG) for funding the project under grant GE 1652/3-2 in the Researcher Unit FOR 2363.
PY - 2021
Y1 - 2021
N2 - The building industry is benefited by building performance simulation (BPS) for design assistance. Machine learning (ML) has been widely used for quick performance prediction; however, it lacks the flexibility to scale for new designs. By spatially and semantically decomposing the building design into components, this article links the ML approach with the system engineering paradigm of BPS to develop component-based machine learning (CBML). While previous use of CMBL focused on point predictions, this study proves that the CBML is able to predict dynamic time-series energy performance for new design cases by deriving a set of reusable model components. We trained and tested the ML model on a dataset of 1000 examples. The objective is to ascertain the ability of the ML model to generalize via different decomposition levels. Hourly energy predictions during the design phase are useful for equipment sizing, controlling peak energy demands, and leveling the load in the networks.
AB - The building industry is benefited by building performance simulation (BPS) for design assistance. Machine learning (ML) has been widely used for quick performance prediction; however, it lacks the flexibility to scale for new designs. By spatially and semantically decomposing the building design into components, this article links the ML approach with the system engineering paradigm of BPS to develop component-based machine learning (CBML). While previous use of CMBL focused on point predictions, this study proves that the CBML is able to predict dynamic time-series energy performance for new design cases by deriving a set of reusable model components. We trained and tested the ML model on a dataset of 1000 examples. The objective is to ascertain the ability of the ML model to generalize via different decomposition levels. Hourly energy predictions during the design phase are useful for equipment sizing, controlling peak energy demands, and leveling the load in the networks.
UR - http://www.scopus.com/inward/record.url?scp=85134232723&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85134232723
T3 - EG-ICE 2021 Workshop on Intelligent Computing in Engineering, Proceedings
SP - 34
EP - 43
BT - EG-ICE 2021 Workshop on Intelligent Computing in Engineering, Proceedings
A2 - Abualdenien, Jimmy
A2 - Borrmann, Andre
A2 - Ungureanu, Lucian-Constantin
A2 - Hartmann, Timo
T2 - 28th International Workshop on Intelligent Computing in Engineering of the European Group for Intelligent Computing in Engineering, EG-ICE 2021
Y2 - 30 June 2021 through 2 July 2021
ER -