Comparative study of metamodelling techniques in building energy simulation: Guidelines for practitioners
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ML has the potential to greatly speed up building energy demand computations by ignoring physical knowledge, incorporating it, or learning surrogate models.
“ML has the potential to speed up these computations greatly, either by ignoring physical knowledge of the building entirely [452,617], by incorporating it into the computation [195], or by learning to approximate the physical model to reduce the need for expensive simulation (surrogate models) [274].”
From Tackling climate change with machine learning (2023) · cites this paper as [274]
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Research works printed but not linked: 1
- J. P. Kleijnen, R. G. Sargent, A methodology for fitting and validating metamodels in simulation, European Journal Of Operational Research 120 (2000) 14-29.
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Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works
- Authors
- Van Gelder, L., Das, P., Janssen, H., & Roels, S.
- Affiliations
- University College London (GB)
- KU Leuven (BE)
- Venue
- Simulation Modelling Practice and Theory
- Publisher
- Elsevier BV
- Published
- 2014-12
- Volume
- 49
- Pages
- 245-257
- ISSN
- 1569-190X (ISSN-L)
- OpenAlex
- W2002322798
- Cited by
- 123
- References
- 34
- Citations by year
- 2026: 5 · 2025: 9 · 2024: 14 · 2023: 13 · 2022: 19 · 2021: 10 · 2020: 13 · 2019: 14 · 2018: 11 · 2017: 4 · 2016: 4 · 2015: 7
- FWCI
- 7.41
- Type
- journal article
- Language
- en
- Open access
- green
- lirias.kuleuven.be
- Licence
- Locations
- Simulation Modelling Practice and Theory (publishedVersion)
- Lirias (submittedVersion, other-oa)
- Funders
- Vrije Universiteit Brussel
- Topics
- Physical Sciences › Engineering › Building and Construction › Building Energy and Comfort Optimization
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Advanced Multi-Objective Optimization Algorithms
- Social Sciences › Decision Sciences › Management Science and Operations Research › Simulation Techniques and Applications
- Keywords
- metamodeling, surrogate models, building energy simulation, kriging, multivariate adaptive regression splines, polynomial regression, radial basis function neural network, artificial neural networks, computational complexity reduction, model selection, design optimization
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