Comparison of Sensitivity Analysis Methods in Building Energy Assessment

Song, Yang; Tian, Wenxi; Cubí, Eduard; Qing-xin, Meng; Liu, YunLiang; Lai, Wei Kuang

doi:10.1016/j.proeng.2016.06.369

Cited by 65 publications

(21 citation statements)

References 8 publications

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“…General-purpose programming languages can be used to edit building energy programs to automatically create many building energy models, including Matlab [3], Python [121], Excel VBA [2,133], and R environment [134,135]. Tian and de Wilde [2] used the Excel visual basic application (VBA) to create a large number of EnergyPlus models to investigate the impact of climate change on building thermal performance.…”

Section: Create and Run Energy Modelsmentioning

confidence: 99%

“…Sensitivity analysis can be divided into local and global sensitivity analysis [36]. Detailed descriptions on the application of sensitivity analysis in building performance assessment can be found in [36,134]. Dominguez-Munoz et al [116] used the SRC to determine the key factors affecting peak cooling loads.…”

Section: Conduct Sensitivity Analysismentioning

confidence: 99%

“…The Simlab program [191] mainly contains a set of global sensitivity analysis based on sampling-based methods, such as standardized (rank) regression coefficient, Morris method, and Sobol sensitivity indicators. The Simlab program has been used widely in building energy analysis [72,113,114,134]. The latest version of Simlab 4 provides a closer link to the R statistical environment [191].…”

Section: Software For Uncertainty Analysismentioning

confidence: 99%

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A review of uncertainty analysis in building energy assessment

Tian

Heo

Wilde

et al. 2018

Renewable and Sustainable Energy Reviews

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337

122

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Uncertainty analysis in building energy assessment has become an active research field because a number of factors influencing energy use in buildings are inherently uncertain. This paper provides a systematic review on the latest research progress of uncertainty analysis in building energy assessment from four perspectives: uncertainty data sources, forward and inverse methods, application of uncertainty analysis, and available software. First, this paper describes the data sources of uncertainty in building performance analysis to provide a firm foundation for specifying variations of uncertainty factors affecting building energy. The next two sections focus on the forward and inverse methods. Forward uncertainty analysis propagates input uncertainty through building energy models to obtain variations of energy use, whereas inverse uncertainty analysis infers unknown input factors through building energy models based on energy data and prior information. For forward analysis, three types of approaches (Monte Carlo, non-sampling, and non-probabilistic) are discussed to provide sufficient choices of uncertainty methods depending on the purpose and specific application of a building project. For inverse analysis, recent research has concentrated more on Bayesian computation because Bayesian inverse methods can make full use of prior information on unknown variables. Fourth, several applications of uncertainty analysis in building energy assessment are discussed, including building stock analysis, HVAC system sizing, variations of sensitivity indicators, and optimization under uncertainty. Moreover, the software for uncertainty analysis is described to provide flexible computational environments for implementing uncertainty methods described in this review. This paper concludes with the trends and recommendations for further research to provide more convenient and robust uncertainty analysis of building energy. Uncertainty analysis has been ready to become the mainstream approach in building energy assessment although a number of issues still need to be addressed.

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Section: Create and Run Energy Modelsmentioning

confidence: 99%

Section: Conduct Sensitivity Analysismentioning

confidence: 99%

Section: Software For Uncertainty Analysismentioning

confidence: 99%

See 1 more Smart Citation

A review of uncertainty analysis in building energy assessment

Tian

Heo

Wilde

et al. 2018

Renewable and Sustainable Energy Reviews

Self Cite

337

122

View full text Add to dashboard Cite

show abstract

“…When feasible, the most rigorous approach may be that of applying two or more sensitivity analysis methods to increase confidence in the results, as proposed by Tian and de Wilde [22] and Yang et al [31].…”

Section: Brief Introduction To Sensitivity Analysismentioning

confidence: 99%

Empirical validation and local sensitivity analysis of a lumped-parameter thermal model of an outdoor test cell

Cattarin

Pagliano

Causone

et al. 2018

Building and Environment

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This paper presents the experimental validation of a thermal model describing the ZEB Test Cells Laboratory, located at the Gløshaugen campus of NTNU and SINTEF in Trondheim, Norway. Besides, a local sensitivity analysis identifies the parameters and inputs that are most influential on the thermal behaviour of the test cell, in terms of temperature profiles of the internal air and internal surfaces. The analysis shows that, in free-running conditions, the most important parameters and inputs, out of the 49 tested ones, are: the air temperature in the guard zone, the initial temperature(s) of the test cell envelope, the linear dimension of the square window, the solar irradiance on the vertical plane of the window, the depth of the test cell, the thermal conductivity and the thickness of the polyurethane layer in the envelope, the solar direct transmittance of the window, the internal height and width of the test cell, the external air temperature and the electrical power input to the mixing fan. Based on the outcome of the local sensitivity analysis and on in-field observations, some practical measures to improve the quality of the input data provided to a dynamic energy simulation tool, and thus the accuracy of prediction of the temperature evolution of the test cell. For example, we suggest monitoring accurately the environmental conditions in the guard zone, which are particularly influential under free-running conditions, and installing a global irradiance pyranometer next to the window in order to reduce the uncertainty related to the entering solar load.

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“…The authors also recommended the Morris method to be used to exclude unimportant variables, but not to rank the most influent ones, as it differed from the other approaches. Yang et al (2016) also performed a comparison between four sensitivity analysis methods such as the SRC, Morris, FAST and the treed Gaussian process (TGP) − a meta-modelling-based sensitivity analysis. The authors applied the methods in two commercial buildings in China with different aspect ratios.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of capabilities of different global sensitivity analysis techniques for building energy simulation: experiment on design variables

Silva

Ghisi

2021

Ambient. constr.

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The objective of this study is to investigate the capabilities of different global sensitivity analysis methods applied to building performance simulation, i.e. Morris, Monte Carlo, Design of Experiments, and Sobol methods. A single-zone commercial building located in Florianópolis, southern Brazil, was used as a case study. Fifteen inputs related to design variables were considered, such as thermal properties of the construction envelope, solar orientation, and fenestration characteristics. The performance measures were the annual heating and cooling loads. It was found that each method can provide different visual capabilities and measures of interpretation, but, in general, there was little difference in showing the most influent and least influent variables. For the heating loads, the thermal transmittances were the most influent variables, while for the cooling loads, the solar absorptances stood out. The Morris method showed to be the most feasible method due to its simplicity and low computational cost. However, as the building simulation model is still complex and non-linear, the variance-based method such as the Sobol is still necessary for general purposes.

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Comparison of Sensitivity Analysis Methods in Building Energy Assessment

Cited by 65 publications

References 8 publications

A review of uncertainty analysis in building energy assessment

A review of uncertainty analysis in building energy assessment

Empirical validation and local sensitivity analysis of a lumped-parameter thermal model of an outdoor test cell

Evaluation of capabilities of different global sensitivity analysis techniques for building energy simulation: experiment on design variables

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