E3S Web Conf.
Volume 362, 2022BuildSim Nordic 2022
|Number of page(s)||8|
|Section||Buildings, Districts and Energy|
|Published online||01 December 2022|
- Antoniadis, A., Lambert-Lacroix, S., & Poggi, J. M. (2021). Random forests for global sensitivity analysis: A selective review. Eng. & Sys. Safety, 206, 107312. [CrossRef] [Google Scholar]
- de Wilde, P. (2014). The gap between predicted and measured energy performance of buildings: A framework for investigation. Automation in construction, 41, 40–49. [CrossRef] [Google Scholar]
- Kucherenko, S., Albrecht, D., & Saltelli, A. (2015). Exploring multi-dimensional spaces: A comparison of Latin hypercube and quasi Monte Carlo sampling techniques. arXivpreprint arXiv:1505.02350. [Google Scholar]
- Loeppky, J. L., Sacks, J., & Welch, W. J. (2009). Choosing the sample size of a computer experiment: A practical guide. Technometrics, 51(4), 366–376. [Google Scholar]
- Morris, M. D. (1991). Factorial sampling plans for preliminary computational experiments. Technometrics, 33(2), 161–174. [Google Scholar]
- Pang, Z., O'Neill, Z., Li, Y., & Niu, F. (2020). The role of sensitivity analysis in the building performance analysis: A critical review. Energy and Buildings, 209, 109659. [CrossRef] [Google Scholar]
- Ruano, M. V., Ribes, J., Seco, A., & Ferrer, J. (2012). An improved sampling strategy based on trajectory design for application of the Morris method to systems with many input factors. Env. Mod. & Softw., 37, 103–109. [CrossRef] [Google Scholar]
- Saltelli, A., Tarantola, S., Campolongo, F., & Ratto, M. (2004). Sensitivity analysis in practice: a guide to assessing scientific models. New York: Wiley. [Google Scholar]
- Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D. & Tarantola, S. (2008). Global sensitivity analysis: The primer. John Wiley & Sons. [Google Scholar]
- Saltelli, A., Aleksankina, K., Becker, W., Fennell, P., Ferretti, F., Holst, N. & Wu, Q. (2019). Why so many published sensitivity analyses are false: A systematic review of sensitivity analysis practices. Environmental modelling & software, 114, 29–39. [CrossRef] [Google Scholar]
- Sanchez, D. G., Lacarrière, B., Musy, M., & Bourges, B. (2014). Application of sensitivity analysis in building energy simulations: Combining first-and second-order elementary effects methods. Energy and Buildings, 68, 741–750. [CrossRef] [Google Scholar]
- Tian, W. (2013). A review of sensitivity analysis methods in building energy analysis. Renewable and sustainable energy reviews, 20, 411–419. [Google Scholar]
- Tian, W., Heo, Y., de Wilde, P., Li, Z., Yan, D., Park, C. S. & Augenbroe, G. (2018). A review of uncertainty analysis in building energy assessment. Renewable and Sustainable Energy Reviews, 93, 285–301. [Google Scholar]
- Van Gelder, L., Janssen, H., & Roels, S. (2014). Probabilistic design and analysis of building performances: Methodology and application example. Energy and Buildings, 79, 202–211. [CrossRef] [Google Scholar]
- Van Gelder, L., Das, P., Janssen, H., & Roels, S. (2014b). Comparative study of metamodelling techniques in building energy simulation: Guidelines for practitioners. Sim. Mod. Prac. & Theory, 49, 245–257. [Google Scholar]
- Østergård, T., Jensen, R. L., & Maagaard, S. E. (2016). Building simulations supporting decision making in early design-A review. Renewable and Sustainable Energy Reviews, 61, 187–201. [Google Scholar]
- Østergård, T., Jensen, R. L., & Maagaard, S. E. (2017). Early Building Design: Informed decision-making by exploring multidimensional design space using sensitivity analysis. Energy and Buildings, 142, 8–22. [CrossRef] [Google Scholar]
- Østergård, T., Jensen, R. L., & Maagaard, S. E. (2018). A comparison of six metamodeling techniques applied to BPS. Applied Energy, 211, 89–103. [CrossRef] [Google Scholar]
- Østergård, T., Lindgren, L. B., & Jensen, R. L. (2020). The right way to do building simulations? Using Monte Carlo simulations, sensitivity analysis, and metamodeling on a design case. In Int. Conf. IBPSA-Nordic, 13th-14th Oct 2020, BuildSIM-Nordic 2020. [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.