< Back to previous page
Researcher
Matthias Van Hove
- Disciplines:Building physics, Classical thermodynamics, heat transfer, Statistical data science, Stochastic analysis and modelling, Building technology, Energy in buildings and built environments, Sustainable buildings and cities, Urban physics
Affiliations
- Department of Architecture and urban planning (Department)
Member
From25 Jul 2018 → Today
Projects
1 - 2 of 2
- Bottom-up dynamic grey-box modelling: Scalable data-driven methods for predicting real energy-saving potential and promoting actionable energy-saving strategiesFrom1 Oct 2025 → TodayFunding: FWO junior postdoctoral fellowship
- Bottom-up engineering building stock models: closing the performance gap using grey-box modellingFrom1 Oct 2018 → 30 Sep 2022Funding: BOF - Doctoral projects
Publications
1 - 10 of 21
- Quality assurance for building-stock energy models : a performance comparison of eleven uncertainty and sensitivity analysis methods(2024)Published in: JOURNAL OF BUILDING PERFORMANCE SIMULATIONISSN: 1940-1507Issue: 2Volume: 17Pages: 149 - 175
- Large-scale statistical analysis and modelling of real and regulatory total energy use in existing single-family houses in Flanders(2023)Published in: BUILDING RESEARCH AND INFORMATIONISSN: 1466-4321Issue: 2Volume: 51Pages: 203 - 222
- In-situ empirical validation of common indoor climate parameters in an inhabited multizone dwelling(2023)Published in: SCIENCE AND TECHNOLOGY FOR THE BUILT ENVIRONMENTISSN: 2374-474XIssue: 7Volume: 29Pages: 730 - 747
- Increasing reliability of bottom-up building-stock energy models using available data-driven techniques(2023)Pages: XXVI, 475 p.
- Uncertainty and sensitivity analysis of building-stock energy models : sampling procedure, stock size and Sobol’ convergence(2023)Published in: JOURNAL OF BUILDING PERFORMANCE SIMULATIONISSN: 1940-1507Issue: 6Volume: 16Pages: 749 - 771
- Comparison of global sensitivity analysis methods for urban scale building stock energy models(2022)Number of pages: 1
- Data-driven statistical and machine learning modelling of real building stock energy use(2022)Number of pages: 1
- Statistical data-driven analysis and modelling of total energy use in new or thoroughly renovated single-family houses(2022)Volume: 362Number of pages: 1
- Indoor climate prediction performance of dynamic BES-models in dymola(2022)Number of pages: 1
- Data-driven machine learning model performance of real annual natural gas consumption in residential buildings(2022)Pages: 181 - 188