Application of sensitivity analysis in building energy simulations: Combining first- and second-order elementary effects methods

Sánchez, David García; Lacarrière, Bruno; Musy, Marjorie; Bourges, Bernard

doi:10.1016/j.enbuild.2012.08.048

Cited by 157 publications

(26 citation statements)

References 21 publications

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“…Figure displays a chart of the standard deviation of elementary effects

σ_{i}

versus the average elementary effects

μ_{i}

as well as their absolute values

μ_{i}^{*}

. Charts are interpreted as follows: The higher

μ_{i}^{*}

, the more influential the parameter High standard deviation values

σ_{i}

(

σ_{i} / μ_{i}^{*} \geq 0.5

) indicate a nonmonotonic behavior or possible interactions between parameters. For

0.1 < σ_{i} / μ_{i}^{*} < 0.5

, the effect of the parameter is considered monotonic, whereas for

σ_{i} / μ_{i}^{*} < 0.1

it is considered linear (Garcia Sanchez et al ). The algebraic sign of

μ_{i}

indicates the increasing (i.e., positive sign) or decreasing (negative sign) trend of the indicator relative to the considered parameter. …”

Section: Resultsmentioning

confidence: 99%

Sensitivity Analysis of Environmental Process Modeling in a Life Cycle Context: A Case Study of Hemp Crop Production

Andrianandraina¹,

Ventura²,

Kiessé³

et al. 2015

J of Industrial Ecology

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International audienceThe aim of this article is to develop a methodological approach allowing to assess the influence of parameters of one or more elementary processes in the foreground system, on the outcomes of a life cycle assessment (LCA) study. From this perspective, the method must be able to: (1) include foreground process modeling in order to avoid the assumption of proportionality between inventory data and reference flows; (2) quantify influences of foreground processes’ parameters (and, possibly, interactions between parameters); and (3) identify trends (either increasing or decreasing) for each parameter on each indicator in order to determine the most favorable direction for parametric variation. These objectives can be reached by combining foreground system modeling, a set of two different sensitivity analysis methods (each one providing different and complementary information), and LCA. The proposed method is applied to a case study of hemp-based insulation materials for buildings. The present study will focus on the agricultural stage as a foreground system and as a first step encompassing the entire life cycle. A set of technological recommendationswere identified for hemp farmers in order to reduce the crop’s environmental impacts (from –11% to –89% according to the considered impact category). One of the main limitations of the approach is the need for a detailed model of the foreground process. Further, the method is, at present, rather time-consuming. However, it offers long-term advantages given that the higher level of model detail adds robustness to the LCA results

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“…Figure displays a chart of the standard deviation of elementary effects

σ_{i}

versus the average elementary effects

μ_{i}

as well as their absolute values

μ_{i}^{*}

. Charts are interpreted as follows: The higher

μ_{i}^{*}

, the more influential the parameter High standard deviation values

σ_{i}

(

σ_{i} / μ_{i}^{*} \geq 0.5

) indicate a nonmonotonic behavior or possible interactions between parameters. For

0.1 < σ_{i} / μ_{i}^{*} < 0.5

, the effect of the parameter is considered monotonic, whereas for

σ_{i} / μ_{i}^{*} < 0.1

it is considered linear (Garcia Sanchez et al ). The algebraic sign of

μ_{i}

indicates the increasing (i.e., positive sign) or decreasing (negative sign) trend of the indicator relative to the considered parameter. …”

Section: Resultsmentioning

confidence: 99%

Sensitivity Analysis of Environmental Process Modeling in a Life Cycle Context: A Case Study of Hemp Crop Production

Andrianandraina¹,

Ventura²,

Kiessé³

et al. 2015

J of Industrial Ecology

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show abstract

“…As the geometric and thermal properties of radiant slabs vary widely in engineering applications, we conducted a sensitivity analysis to identify the contribution of the individual design variables to the performance of the pulsed flow control method. We used the Morris method that is derived from one-factor-at-a-time screening-based methods, used to identify the subset of the main important input factors among a large number of input parameters by ranking input factors [38,39]. In the Morris method, only one parameter varies in every step and the final sensitivity measures are calculated by averaging at different points of the input space.…”

Section: Screening-based Methodsmentioning

confidence: 99%

Performance analysis of pulsed flow control method for radiant slab system

Tang

Raftery

Liu

et al. 2018

Building and Environment

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We present a novel pulsed flow control method (PFM) using a two-position valve to regulate the capacity of radiant slab systems. Under PFM, the on-time duration of the valve is short (compared to all prior work, e.g. 4-minute), and fixed, while the off-time varies. We present a novel, open-source, finite difference model that assesses three-dimensional transient slab heat transfer, accounting for the transient heat storage of the pipe fluid. Sensitivity analysis results indicate the dominant factors influencing energy performance of the PFM are: on-time duration; pipe diameter; and spacing. We experimentally validated both the new control strategy and model in full-scale laboratory experiments. Compared with previous intermittent control strategies (with on-time durations over 30 min), at 50% part load the PFM reduces 27% required water flow rate and increases supply to return water temperature differential. Compared with the variable temperature control method, at 50% part load the PFM reduces 24% required water flow rate. The energy performance of PFM is comparable to that of a conventional variable flow rate control. However, it has more accurate capacity control, achieves a more uniform surface temperature distribution, and reduces initial investment by substituting two-position for modulating valves, thus showing promise for engineering applications.

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“…Application of the method is growing in various fields of science in recent years, such as physics, electronics, and environics [28][29][30]. In particular, sensitivity analysis is also used in building energy efficiency study [31,32]. This method especially aims at deciding the best parameter value for an optimized selection by altering a certain parameter while keeping others remain unchanged [33].…”

Section: Modified Sensitivity Analysismentioning

confidence: 99%

Optimization of Passive Envelop Energy Efficient Measures for Office Buildings in Different Climate Regions of China Based on Modified Sensitivity Analysis

Wang

Zhao

2018

Sustainability

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With the growing desire for new buildings and better indoor comfort, the amount of energy consumption of office buildings in China is increasing rapidly, which will lead to the great challenge for energy supply and sustainable development. Building passive envelop energy efficient measures (PEEEMs), mainly including exterior wall insulation, roof insulation, different glazing types, and shading system, were widely applied. However, the specific energy efficient performance of PEEEMs was various in different climate conditions that have not been evaluated clearly yet. The priority order of PEEEMs was not recommended in relative standards. The economic benefits cannot be considered synchronously with PEEEMs optimization. This paper modified the sensitivity analysis to fit the building energy efficiency evaluation. By combining the modified method with a simulation tool, the energy efficiency and economic effects of PEEEMs of office buildings in various climate regions can be considered at the same time. Four case buildings in Shenyang, Tianjin, Ningbo, and Shenzhen were proved to reach the energy-saving potentials of 9.44%, 7.75%, 20.87%, and 13.27% respectively, with the payback period of no more than 1.5 years. Finally, the recommended application priority rankings and the recommended ranges of thermal performance properties of PEEEMs in the four typical climate regions were presented.

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Application of sensitivity analysis in building energy simulations: Combining first- and second-order elementary effects methods

Cited by 157 publications

References 21 publications

Sensitivity Analysis of Environmental Process Modeling in a Life Cycle Context: A Case Study of Hemp Crop Production

Sensitivity Analysis of Environmental Process Modeling in a Life Cycle Context: A Case Study of Hemp Crop Production

Performance analysis of pulsed flow control method for radiant slab system

Optimization of Passive Envelop Energy Efficient Measures for Office Buildings in Different Climate Regions of China Based on Modified Sensitivity Analysis

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