Development of a Metamodelling Framework for Building Energy Models with Application to Fifth-Generation District Heating and Cooling Networks

Nicholas Long, Fatema Almajed, Justus von Rhein, Gregor Henze

Research output: Contribution to journalArticlepeer-review

4 Scopus Citations

Abstract

Fully defined physics-based building energy models can accurately represent building systems; however, generating models based on high-level parameters is time consuming and simulation time of complex models can be slow. This article discusses the development of a Metamodelling Framework to create metamodels from a building energy modelling dataset. The framework generates metamodels using either linear regression, random forests, or support vector regressions. A fifth-generation district heating and cooling system analysis use case was used to motivate the development of the framework. The use case required quick and accurate representations of annual building loads reported hourly. Typical annual building modelling approaches can result in a runtime of 10 min. The metamodels runtime was reduced to less than 10 s to load and run an annual simulation with user-defined covariates. The results of the metamodel performance and an abbreviated topology analysis based on the motivating use case will be presented.

Original languageAmerican English
Pages (from-to)203-225
Number of pages23
JournalJournal of Building Performance Simulation
Volume14
Issue number2
DOIs
StatePublished - 2021

Bibliographical note

Publisher Copyright:
© 2021 International Building Performance Simulation Association (IBPSA).

NREL Publication Number

  • NREL/JA-5500-76857

Keywords

  • building energy modelling
  • commercial buildings
  • fifth-generation district heating and cooling
  • Metamodelling
  • random forests
  • support vector regression

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