Open Access
Issue |
E3S Web Conf.
Volume 172, 2020
12th Nordic Symposium on Building Physics (NSB 2020)
|
|
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Article Number | 22005 | |
Number of page(s) | 8 | |
Section | Energy performance assessment based on in situ measurements incl. IEA Annex 71 | |
DOI | https://doi.org/10.1051/e3sconf/202017222005 | |
Published online | 30 June 2020 |
- D. Yan, W. OBrien, T. Hong, X. Feng, H.B. Gunay, F. Tahmasebi, A. Mahdavi, Occupant behavior mod- eling for building performance simulation: Current state and future challenges, Energy and Buildings, Vol. 107, pp. 264 – 278 (2015) [Google Scholar]
- L. Tianzhen Hong, Technical report: Studying occu- pant behavior in buildings: Methods and challenges, International Energy Agency, EBC Annex 66 (2017), https://annex66.org/?q=Publication [Google Scholar]
- M. Zuraimi, A. Pantazaras, K. Chaturvedi, J. Yang, K. Tham, S. Lee, Predicting occupancy counts using physical and statistical co2-based modeling method- ologies, Building and Environment, Vol. 123, pp. 517 – 528 (2017) [Google Scholar]
- T. Labeodan, W. Zeiler, G. Boxem, Y. Zhao, Occu- pancy measurement in commercial office buildings for demand-driven control applicationsa survey and detection system evaluation, Energy and Buildings, Vol. 93, pp. 303 – 314 (2015) [Google Scholar]
- IEA ECB Annex 71. Building Energy Performance Assessment Based on In-situ Measurements, Website (2016-2021), https://www.ecbcs.org/projects/project?AnnexID=71 [Google Scholar]
- S. Kim, Y. Song, Y. Sung, D. Seo, Development of a consecutive occupancy estimation framework for im- proving the energy demand prediction performance of building energy modeling tools, Energies, Vol. 12, 433. (2019) [Google Scholar]
- T.H. Pedersen, K.U. Nielsen, S. Petersen, Method for room occupancy detection based on trajectory of in- door climate sensor data, Building and Environment, Vol. 115, pp. 147 – 156 (2017) [Google Scholar]
- S.H. Ryu, H.J. Moon, Development of an occupancy prediction model using indoor environmental data based on machine learning techniques, Building and Environment, Vol. 107, pp. 1 – 9 (2016) [Google Scholar]
- C. Jiang, M.K. Masood, Y.C. Soh, H. Li, Indoor occu- pancy estimation from carbon dioxide concentration, Energy and Buildings, Vol. 131, pp. 132 – 141 (2016) [Google Scholar]
- L. Breiman, Random forests, Machine Learning, Vol. 45, pp. 5–32 (2001) [Google Scholar]
- EN 14240:2004-04 Ventilation for buildings - Chilled ceilings - Testing and rating (2004) [Google Scholar]
- VDI 2078:2015-06 Calculation of thermal loads and room temperatures (design cooling load and annual simulation) (2004) [Google Scholar]
- M.N. Wright, A. Ziegler, ranger: A fast implementa- tion of random forests for high dimensional data in C++ and R, Journal of Statistical Software, Vol. 77, pp. 1–17 (2017) [Google Scholar]
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