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Author(s):	Zorn, Max; Catunda, Natasha; Claus, Luisa; Kobylinska, Natalia; Frey, Manuel; Wortmann, Thomas
Title:	Replacing energy simulations with surrogate models for design space exploration
Abstract:	Sustainable design requires holistic decision making already in the feasibility stage. One critical aspect of a buildings' sustainability is its operational energy consumption, but energy simulations typically are too time-consuming for early, fast-paced design phases. Data-based, Machine Learning models - so called surrogates - can replace time-consuming simulations with real-time estimates. This paper investigates the accuracy of different surrogate models for energy performance by comparing different types of models and numbers of samples. The paper also presents an integrated dashboard for holistic decision making as an application of the developed surrogate. Surrogat-Modelle als Ersatz für Gebäudesimulationen während der Entwurfsraumerkundung Bereits in frühen Leistungsphasen erfordert nachhaltiges Entwerfen eine ganzheitliche Entscheidungsfindung. Dabei bildet der Energiebedarf eines Gebäudes einen wichtigen Teil in dessen Nachhaltigkeit, jedoch sind Gebäudesimulationen meist zu zeitaufwändig für die frühe, dynamische Entwurfsphase. Auf Maschinellem Lernen beruhende, datenbasierte Ersatzmodelle, sogenannte Surrogat-Modelle, können in Echtzeit Schätzwerte liefern und somit langsame Simulationen ersetzen. Dieser Aufsatz untersucht die Genauigkeit verschiedener Surrogat-Modelle für Gebäudesimulationen durch Vergleich diverser Algorithmen und Anzahlen verwendeter Datenpunkte. Die Anwendung der entwickelten Surrogate wird durch die Integration in ein Visualisierungswerkzeug zur ganzheitlichen Entscheidungsfindung in frühen Leistungsphasen demonstriert.
Source:	Bauphysik 44 (2022), No. 6
Page/s:	311-316
Language of Publication:	German

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