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

verfasst von

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.

Externe Organisation(en)

Technische Universität München (TUM)
KU Leuven

Typ

Paper

Seiten

106-113

Anzahl der Seiten

8

Publikationsdatum

2015

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Angewandte Informatik, Architektur, Modellierung und Simulation, Bauwesen

Ziele für nachhaltige Entwicklung

SDG 13 – Klimaschutzmaßnahmen