Quantification of uncertainty in predicting building energy consumption: A stochastic approach

Brohus, Henrik; Frier, Christian; Heiselberg, Per; Haghighat, Fariborz

doi:10.1016/j.enbuild.2012.07.013

Cited by 37 publications

(22 citation statements)

References 10 publications

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“…In one study, about 100 of the 1009 input parameters of a building model had statistical significance (Eisenhower et al 2011). Brohus et al (2012) quantified the uncertainty of building energy consumption using stochastic differential equations and applied the method to an arbitrary number of loads and zones in a building. Burhenne et al (2013) proposed a cost-benefit analysis using an MCA with Monte Carlo filtering to find which variables drive model uncertainty.…”

Section: Literature Reviewmentioning

confidence: 99%

A methodology for identifying the influence of design variations on building energy performance

Bucking

Zmeureanu

Athienitis

2013

Journal of Building Performance Simulation

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In building performance simulation, understanding the potential for parameter variations to cause a disproportionately large change in a performance metric is an important aspect of the modelling and design process. This is especially true if the proposed building is expected to meet a performance target such as net-zero energy consumption. In the context of this paper, variations refer to design modifications which lead to large changes in a performance metric. This paper proposes a methodology to identify influential variations around a performance criterion. This methodology aids in the understanding of possible discrepancies between predicted and realized building performance. A net-zero energy house case-study demonstrates the methodology. A variability analysis of the case-study indicated that combinations of variations caused energy consumption to be larger than on-site generation in 20% of variational scenarios. A back-tracking search identified that 8 of 26 variables were responsible for significant changes in net-energy consumption. In particular, energy-related occupant behaviour, solar orientation, and variables related to the sizing of a roof-based photovoltaic system can significantly influence net-energy consumption. The case-study helped quantify two optimal approaches for passive solar design -one relying on high insulation levels and lower window areas, and the other relying on good insulation levels and large window areas.

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Section: Literature Reviewmentioning

confidence: 99%

A methodology for identifying the influence of design variations on building energy performance

Bucking

Zmeureanu

Athienitis

2013

Journal of Building Performance Simulation

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“…A hybrid physical-statistical approach is described in [68], where stochastic parameters are introduced into the physical model and the statistical time series model is formulated to reflect model 10 uncertainties, while a methodology based on Bayesian calibration of the normative EN ISO 13790 energy models is presented in [69], focusing on model parameter uncertainty quantification to generate probabilistic predictions of retrofit performances. The uncertainty is also quantified in [70] by means of stochastic differential equations applied to a general heat balance for an arbitrary number of loads and zones in a building, to determine the dynamic thermal response under random 15 conditions. Uncertainty in energy consumption due to actual weather and building operational practices is investigated in [71], using simulation-based analysis of a medium size office building and Monte Carlo sampling of possible parameter combinations.…”

Section: Grey Box / Hybrid Modelsmentioning

confidence: 99%

Suitability analysis of modeling and assessment approaches in energy efficiency in buildings

Koulamas

Καλογεράς

Pacheco-Torres

et al. 2018

Energy and Buildings

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The widely accepted importance of energy efficiency in the building sector is continuously acknowledged by the engineering and research community, as proven by the quantity and diversification of relevant modeling proposals in literature. It is often difficult to collect and assess 20 this plethora of approaches and sometimes the diversity of the features of the available options makes it hard to decide what is the most convenient for the purpose required. This work presents a comprehensive analysis of the most important results today, along with their various classification and assessment approaches for modeling energy building consumption. A critical review of the limitations of the different existing approaches is conducted, and open research challenges are also 25highlighted. Finally, a horizontal and selective assessment of their suitability according to a descriptive set of qualitative comparison contexts and parameters is provided.

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“…Taylor decomposition is a simple method that cannot be used in the case of a very non-smooth model because of the risk of having inaccurate approximations and incorrect results. Brohus [20] applied Taylor decomposition to determine the uncertainty of heat losses through natural ventilation by coupling the local sensitivity method with a CFD model. These results are compared with a Monte Carlo approach, and there is no significant difference.…”

Section: Uncertainty Analysismentioning

confidence: 99%

A comparison of methods for uncertainty and sensitivity analysis applied to the energy performance of new commercial buildings

Rivalin

Stabat

Marchio

et al. 2018

Energy and Buildings

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A comparison of methods for uncertainty and sensitivity analysis applied to the energy performance of new commercial buildings Permalink https://escholarship.org/uc/item/0wd4303g Journal Energy and Buildings, 166 a b s t r a c t An Energy Performance Contracting (EPC) is a financing agreement offered by general contractors that enables cost savings from reduced energy consumption to building owners. To create such an offer, the contractor has to provide an energy consumption threshold and a measurement plan.This article aims to draw some recommendations to choose an appropriate approach to provide the information necessary to create the contract, regarding computation time budget, expected accuracy and type of information provided.To get these results, we couple thermal simulations to various uncertainty and sensitivity methods. We first compare screening and differential sensitivity to reduce the number of inputs of the statistical study. Then, we analyze various uncertainty analysis methods to set an appropriate energy consumption threshold, considering the input uncertainties and the study context (Quadratic combination, directional and importance sampling and reliability methods).Sensitivity analyses in various input spaces are then carried out to identify the most critical contributors to energy levels to create the measurement plan. Finally, two metamodeling approaches are tested to reduce the overall computational time: Kriging and sparse polynomial chaos.These methods are tested and compared on a 40 0 0 m ² office building in Nantes, France. The resulting recommendations can be applied to any building, depending on the model regularity, the number of uncertain parameters and the objective of the study.

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Quantification of uncertainty in predicting building energy consumption: A stochastic approach

Cited by 37 publications

References 10 publications

A methodology for identifying the influence of design variations on building energy performance

A methodology for identifying the influence of design variations on building energy performance

Suitability analysis of modeling and assessment approaches in energy efficiency in buildings

A comparison of methods for uncertainty and sensitivity analysis applied to the energy performance of new commercial buildings

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