The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Building model calibration using energy and environmental data
Open accessAbstract
A large number of randomly interacting variables combine to dictate the energy performance of a building. Building energy simulation models attempt to capture these perturbations as accurately as possible. The prediction accuracy of building energy models can now be better examined given the widespread availability of environmental and energy monitoring equipment and reduced data storage costs. In this paper a set of two calibrated environmental sensors together with a weather station are deployed in a 5-storey office building to examine the accuracy of an EnergyPlus virtual building model. Using American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) Guide 14 indices the model was calibrated to achieve Mean Bias Error (MBE) values within ±5% and Cumulative Variation of Root Mean Square Error (CV(RMSE)) values below 10%. The calibrated EnergyPlus model was able to predict annual hourly space air temperatures with an accuracy of ±1.5 °C for 99.5% and an accuracy of ±1 °C for 93.2% of the time.
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Cites
Bayesian calibration of computer models
Kennedy, M. C., & O'Hagan, A. (2001) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/1467-9868.00294 · Cited by 4,222
Modeling of end-use energy consumption in the residential sector: A review of modeling techniques
Swan, L. G., & Ugursal, V. I. (2009) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2008.09.033 · Cited by 1,934
Energy Simulation in Building Design
Clarke, J. (2007) · Routledge eBooks · doi:10.4324/9780080505640 · Cited by 964
A review of methods to match building energy simulation models to measured data
Coakley, D., Raftery, P., & Keane, M. (2014) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2014.05.007 · Cited by 889
A generalised stochastic model for the simulation of occupant presence
Page, J., Robinson, D., Morel, N., & Scartezzini, J. (2007) · Energy and Buildings · doi:10.1016/j.enbuild.2007.01.018 · Cited by 643
Uncertainty analysis in building performance simulation for design support
Hopfe, C. J., & Hensen, J. L. M. (2011) · Energy and Buildings · doi:10.1016/j.enbuild.2011.06.034 · Cited by 457
Methodology to estimate building energy consumption using EnergyPlus Benchmark Models
Fumo, N., Mago, P., & Luck, R. (2010) · Energy and Buildings · doi:10.1016/j.enbuild.2010.07.027 · Cited by 356
Analysis of uncertainty in building design evaluations and its implications
de Wit, S., & Augenbroe, G. (2002) · Energy and Buildings · doi:10.1016/s0378-7788(02)00070-1 · Cited by 317
Calibrating whole building energy models: An evidence-based methodology
Raftery, P., Keane, M. M., & O’Donnell, J. (2011) · Energy and Buildings · doi:10.1016/j.enbuild.2011.05.020 · Cited by 291
Reddy, T. A. (2006) · ASHRAE winter conference papers · Cited by 253
Cited by
Aftab, M., Chen, C., Chau, C.-K., & Rahwan, T. (2017) · Energy and Buildings · doi:10.1016/j.enbuild.2017.07.077 · Cited by 201
Nadal, A., Llorach-Massana, P., Cuerva, E., López-Capel, E., Montero, J. I., Josa, A., Rieradevall, J., & Royapoor, M. (2017) · Applied Energy · doi:10.1016/j.apenergy.2016.11.051 · Cited by 145
Self-citationBlanco, J. M., Buruaga, A., Rojí, E., Cuadrado, J., & Pelaz, B. (2016) · Energy and Buildings · doi:10.1016/j.enbuild.2015.11.053 · Cited by 89
Baba, F. M., Ge, H., Zmeureanu, R., & Wang, L. (. (2022) · Building and Environment · doi:10.1016/j.buildenv.2021.108518 · Cited by 78
Qin, H., & Pan, W. (2020) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2020.120041 · Cited by 66
Metadata
- Authors
- Royapoor, M., & Roskilly, T.
- Venue
- Energy and Buildings
- Publisher
- Elsevier BV
- Published
- 2015-05
- Volume
- 94
- Pages
- 109-120
- ISSN
- 0378-7788 (ISSN-L)
- OpenAlex
- W2029463080
- Cited by
- 277
- References
- 32
- FWCI
- 34.26
- Type
- journal article
- Language
- en
- Open access
- hybrid
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
