Comparison of different meta model approches with a detailed buiding model for long-Term simulations

authored by

Johannes Maderspacher, Philipp Florian Geyer, Thomas Auer, Werner Lang

Abstract

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.

External Organisation(s)

Technical University of Munich (TUM)
KU Leuven

Type

Paper

Pages

106-113

No. of pages

8

Publication date

2015

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

Computer Science Applications, Architecture, Modelling and Simulation, Building and Construction

Sustainable Development Goals

SDG 13 - Climate Action