Generation of stochastic weather data for uncertainty and sensitivity analysis of a low-energy building

Goffart, Jeanne; Mara, Thierry A.; Wurtz, Étienne

doi:10.1177/1744259116668598

Cited by 19 publications

(12 citation statements)

References 19 publications

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“…To perform a more exhaustive assessment of the R eq estimations under variable weather conditions, the proposed methodology is now applied to a set of 2000 synthetic weather datasets with which a variance-based sensitivity analysis is possible [29].…”

Section: Stochastic Weather Data To Perform Global Sensitivity Analysismentioning

confidence: 99%

“…A total of six weather variables are stochastically generated to be representative of the usual weather conditions in Geneva in winter, following the methodology described in [29], as a time series constructed by a combination of statistical and deterministic features. The characteristics are extracted on the basis of the IW EC weather data file (International Weather for Energy Calculations) [30] from Geneva.…”

Section: Stochastic Weather Data To Perform Global Sensitivity Analysismentioning

confidence: 99%

“…In the end, the monthly dataset chosen is the one that shows statistics closest to the mean, median of the 30-year data distribution, after having discarded years with exceptionally long periods of consecutive warm, cold or low radiation days. The stochastic generation [29] thus contains as much variability than in the T M Y file: if the T M Y has for one particular variable a lower variability than the rest of the 30-year actual weather data, it will reflect this in the synthetic data.…”

Section: Stochastic Weather Data To Perform Global Sensitivity Analysismentioning

confidence: 99%

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Influence of natural weather variability on the thermal characterisation of a building envelope

et al. 2021

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The thermal characterisation of a building envelope is usually best performed from on-site measurements with optimised controlled indoor conditions. Conversely, occupant-friendly measurement conditions provide less informative data. Notwithstanding occupancy, the boundary conditions alone contribute to a greater extent to the energy balance, which implies that non-intrusive conditions bring into question the reproducibility and relevance of such measurement. This paper proposes an original numerical methodology to assess the reproducibility and accuracy of the estimation of the overall thermal resistance of an envelope under variable weather conditions. A comprehensive building energy model serves as reference model to produce synthetic data mimicking non-intrusive conditions, each with a different weather dataset. An appropriate model is calibrated from the synthetic data and provides a thermal resistance estimate. The accuracy of the estimates is then assessed in light of the particular weather conditions used for data generation. The originality also lies in the set of weather data that allow for uncertainty and global sensitivity analyses of all estimates with respect to six weather variables. The methodology is applied to a one-storey house reference model, for which thermal resistance is inferred from calibrated RC models. Robust estimations are achieved within 11 days. The outdoor temperature and the wind speed are highly influential because of the large air change rate in the case study.

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Section: Stochastic Weather Data To Perform Global Sensitivity Analysismentioning

confidence: 99%

Section: Stochastic Weather Data To Perform Global Sensitivity Analysismentioning

confidence: 99%

Section: Stochastic Weather Data To Perform Global Sensitivity Analysismentioning

confidence: 99%

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Influence of natural weather variability on the thermal characterisation of a building envelope

et al. 2021

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“…A previous study [31], not presented here, has focused on the statistical analysis of the weather data. One month of january representative of typical winter from 20 years of observations has been used, since the heat consumption is the highest and the most costly, during this period of time, at this place.…”

mentioning

confidence: 99%

“…Moreover, considering only one season allows to respect stationarity of data. From the study in [31], the statistical characteristics of a given input, the covariance function and the hourly mean, on one day, has been determined.…”

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confidence: 99%

Sensitivity analysis of complex models: Coping with dynamic and static inputs

Anstett-Collin

Goffart

Mara³

et al. 2015

Reliability Engineering & System Safety

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. Sensitivity analysis of complex models: coping with dynamic and static inputs. Reliability Engineering and System Safety, Elsevier, 2015, 134, pp.268-275 AbstractIn this article, we address the issue of conducting a sensitivity analysis of complex models with both static and dynamic uncertain inputs. While several approaches have been proposed to compute the sensitivity indices of the static inputs (i.e. parameters), the ones of the dynamic inputs (i.e. stochastic fields) have been rarely addressed. For this purpose, we first treat each dynamic as a Gaussian process. Then, the truncated Karhunen-Loève expansion of each dynamic input is performed. Such an expansion allows to generate independent Gaussian processes from a finite number of independent random variables. Given that a dynamic input is represented by a finite number of random variables, its variance-based sensitivity index is defined by the sensitivity index of this group of variables. Besides, an efficient samplingbased strategy is described to estimate the first-order indices of all the input factors by only using two input samples. The approach is applied to a buildPreprint submitted to Reliability Engineering and System Safety August 5, 2014ing energy model, in order to assess the impact of the uncertainties of the material properties (static inputs) and the weather data (dynamic inputs) on the energy performance of a real low energy consumption house.

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Unraveling the influence of convenience situational factors on e‐waste recycling behaviors: A goal‐framing theory approach

Puzzo,

Prati

2024

Sustainable Development

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The surge in e‐waste production highlights the need to investigate what influences recycling behaviors, ultimately to enhance sustainable management practices. Previous research has explored its psychological antecedents, but there has been limited study on how situational factors of collection centers affect e‐waste recycling behavior. This research investigates how convenience of collection centers (i.e., proximity, availability, and user experience) influences e‐waste recycling behaviors. A survey administered to 700 citizens explores the relationship between convenience and goal‐framing theory components, namely gain, normative, and hedonic goals. Structural equation modeling is employed for analysis. Results reveal that convenience significantly affects e‐waste recycling behaviors through gain (β = −.304, p < .001) and normative goals (β = .154, p < .05), while hedonic goals show no significant association (β = −.064, p > .05). Furthermore, convenience exhibits a direct impact on e‐waste recycling behaviors (β = .166, p < .05). Implications for research and practical strategies for recycling initiatives are discussed. This study adds valuable insights to the existing literature and informs targeted interventions for sustainable e‐waste management.

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Generation of stochastic weather data for uncertainty and sensitivity analysis of a low-energy building

Cited by 19 publications

References 19 publications

Influence of natural weather variability on the thermal characterisation of a building envelope

Influence of natural weather variability on the thermal characterisation of a building envelope

Sensitivity analysis of complex models: Coping with dynamic and static inputs

Unraveling the influence of convenience situational factors on e‐waste recycling behaviors: A goal‐framing theory approach

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