Details
| Original language | English |
|---|---|
| Pages (from-to) | 1439-1453 |
| Number of pages | 15 |
| Journal | Applied energy |
| Volume | 228 |
| Early online date | 13 Jul 2018 |
| Publication status | Published - 15 Oct 2018 |
| Externally published | Yes |
Abstract
Keywords
- Building performance prediction, Building simulation, Component-based machine learning, Parametric systems modeling, Systems engineering
ASJC Scopus subject areas
- Engineering(all)
- Building and Construction
- Engineering(all)
- Mechanical Engineering
- Energy(all)
- General Energy
- Environmental Science(all)
- Management, Monitoring, Policy and Law
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In: Applied energy, Vol. 228, 15.10.2018, p. 1439-1453.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Component-based machine learning for performance prediction in building design
AU - Geyer, Philipp Florian
AU - Singaravel, Sundaravelpandian
N1 - Funding Information: The research was funded by Starting Grant STG-14-00346 of the KU Leuven and by the Deutsche Forschungsgemeinschaft (DFG) in Researcher Unit 2363 “Evaluation of building design variants in early phases using adaptive levels of development”, in Subproject 4 “System-based Simulation of Energy Flows”.
PY - 2018/10/15
Y1 - 2018/10/15
N2 - Machine learning is increasingly being used to predict building performance. It replaces building performance simulation, and is used for data analytics. Major benefits include the simplification of prediction models and a dramatic reduction in computation times. However, the monolithic whole-building models suffer from a limited transfer of models and their data to other contexts. This imposes a vital limitation on the application of machine learning in building design. In this paper, we present a component-based approach that develops machine learning models not only for a parameterized whole building design, but for parameterized components of the design as well. Two decomposition levels, namely construction level components (wall, windows, floors, roof, etc.), and zone-level components, are examined. Results in test cases show that, depending on how far the cases deviate from the training case and its data, high prediction quality may be achieved with errors as low as 3.7% for cooling and 3.9% for heating.
AB - Machine learning is increasingly being used to predict building performance. It replaces building performance simulation, and is used for data analytics. Major benefits include the simplification of prediction models and a dramatic reduction in computation times. However, the monolithic whole-building models suffer from a limited transfer of models and their data to other contexts. This imposes a vital limitation on the application of machine learning in building design. In this paper, we present a component-based approach that develops machine learning models not only for a parameterized whole building design, but for parameterized components of the design as well. Two decomposition levels, namely construction level components (wall, windows, floors, roof, etc.), and zone-level components, are examined. Results in test cases show that, depending on how far the cases deviate from the training case and its data, high prediction quality may be achieved with errors as low as 3.7% for cooling and 3.9% for heating.
KW - Building performance prediction
KW - Building simulation
KW - Component-based machine learning
KW - Parametric systems modeling
KW - Systems engineering
UR - http://www.scopus.com/inward/record.url?scp=85049658932&partnerID=8YFLogxK
U2 - 10.1016/j.apenergy.2018.07.011
DO - 10.1016/j.apenergy.2018.07.011
M3 - Article
AN - SCOPUS:85049658932
VL - 228
SP - 1439
EP - 1453
JO - Applied energy
JF - Applied energy
SN - 0306-2619
ER -