Details
Original language | English |
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Pages | 106-113 |
Number of pages | 8 |
Publication status | Published - 2015 |
Externally published | Yes |
Event | 14th Conference of International Building Performance Simulation Association, BS 2015 - Hyderabad, India Duration: 7 Dec 2015 → 9 Dec 2015 |
Conference
Conference | 14th Conference of International Building Performance Simulation Association, BS 2015 |
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Country/Territory | India |
City | Hyderabad |
Period | 7 Dec 2015 → 9 Dec 2015 |
Abstract
ASJC Scopus subject areas
- Computer Science(all)
- Computer Science Applications
- Engineering(all)
- Architecture
- Mathematics(all)
- Modelling and Simulation
- Engineering(all)
- Building and Construction
Sustainable Development Goals
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2015. 106-113 Paper presented at 14th Conference of International Building Performance Simulation Association, BS 2015, Hyderabad, India.
Research output: Contribution to conference › Paper › Research › peer review
}
TY - CONF
T1 - Comparison of different meta model approches with a detailed buiding model for long-Term simulations
AU - Maderspacher, Johannes
AU - Geyer, Philipp Florian
AU - Auer, Thomas
AU - Lang, Werner
PY - 2015
Y1 - 2015
N2 - If detailed building models are applied for long- Term simulations, for instance the prediction of the future energy demand under climate change, the computational effort can turn into a serious issue. Machine learning algorithms like Neural Networks (NN) or Support Vector Machine (SVM) could be an alternative. In this work a possible application of NN and SVM for long- Term forecasts are proven and their limitations are presented. In the examined case study, with a simulation period over 30 years, the SVM is hundred fifty times and the NN ten times faster than a detailed building model. This reduction of computational effort can be useful for further studies as a uncertainty analysis of climate change.
AB - If detailed building models are applied for long- Term simulations, for instance the prediction of the future energy demand under climate change, the computational effort can turn into a serious issue. Machine learning algorithms like Neural Networks (NN) or Support Vector Machine (SVM) could be an alternative. In this work a possible application of NN and SVM for long- Term forecasts are proven and their limitations are presented. In the examined case study, with a simulation period over 30 years, the SVM is hundred fifty times and the NN ten times faster than a detailed building model. This reduction of computational effort can be useful for further studies as a uncertainty analysis of climate change.
UR - http://www.scopus.com/inward/record.url?scp=84976407036&partnerID=8YFLogxK
M3 - Paper
AN - SCOPUS:84976407036
SP - 106
EP - 113
T2 - 14th Conference of International Building Performance Simulation Association, BS 2015
Y2 - 7 December 2015 through 9 December 2015
ER -