Project: Bottom-up engineering building stock models: closing the performance gap using grey-box modelling
2018-10-01 – 2022-09-30
- Abstract
The aim of this research is to increase reliability and reduce the performance gap of bottom-up engineering building stock models, by combining a large amount of recently available measured data with available data-analysis techniques. The existing theoretical calculation models will be
updated with these techniques and implemented in such a way that they automatically adapt to updates in available data.
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- Journal Article
- A1
- open access
Uncertainty and sensitivity analysis of building-stock energy models : sampling procedure, stock size and Sobol’ convergence
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- Journal Article
- A1
- open access
Large-scale statistical analysis and modelling of real and regulatory total energy use in existing single-family houses in Flanders
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- Conference Paper
- C1
- open access
Data-driven machine learning model performance of real annual natural gas consumption in residential buildings
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- Conference Paper
- C1
- open access
Statistical data-driven analysis and modelling of total energy use in new or thoroughly renovated single-family houses
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- Conference Paper
- C1
- open access
Global sensitivity analysis for building-stock energy models : application of three global approaches
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- Conference Paper
- P1
- open access
Indoor climate prediction performance of dynamic BES-models in dymola
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- Conference Paper
- C1
- open access
Data-driven statistical and machine learning modelling of real building stock energy use
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- Conference Paper
- C3
- open access
Types of uncertainty in simulation models : categorisation for better identification, accounting and assessment
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- Conference Paper
- C3
- open access
Comparison of global sensitivity analysis methods for urban scale building stock energy models
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Coupling of air multizone and thermal multizone models in modelica