Statistical method to identify robust building renovation choices for environmental and economic performance

Galimshina, Alina; Moustapha, Maliki; Hollberg, Alexander; Padey, Pierryves; Lasvaux, Sébastien; Sudret, Bruno; Habert, Guillaume

doi:10.1016/j.buildenv.2020.107143

Cited by 39 publications

(25 citation statements)

References 50 publications

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“…As an application, we consider in this paper, a building situated in Western Switzerland and constructed in 1911 (See Figure 12). The LCA is carried out using a model developed in Galimshina et al (2020). The computational model is implemented in Python and a single run lasts a few seconds.…”

Section: Example 3: Application To Building Renovationmentioning

confidence: 99%

“…The model contains more than a hundred parameters. However, using expert knowledge and global sensitivity analysis, screening allowed us to reduce the input to 23 parameters, among which 6 are design parameters and 13 are environmental variables (Galimshina et al, 2020). The design parameters include 4 categorical variables as shown in Table 1, which lead to 3, 600 levels.…”

Section: Example 3: Application To Building Renovationmentioning

confidence: 99%

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Multi-objective robust optimization using adaptive surrogate models for problems with mixed continuous-categorical parameters

Moustapha,

Galimshina,

Habert

et al. 2022

Preprint

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Explicitly accounting for uncertainties is paramount to the safety of engineering structures.Optimization which is often carried out at the early stage of the structural design offers an ideal framework for this task. When the uncertainties are mainly affecting the objective function, robust design optimization is traditionally considered. This work further assumes the existence of multiple and competing objective functions that need to be dealt with simultaneously. The optimization problem is formulated by considering quantiles of the objective functions which allows for the combination of both optimality and robustness in a single metric. By introducing the concept of common random numbers, the resulting nested optimization problem may be solved using a general-purpose solver, herein the non-dominated sorting genetic algorithm (NSGA-II). The computational cost of such an approach is however a serious hurdle to its application in real-world problems. We therefore propose a surrogate-assisted approach using Kriging as an inexpensive approximation of the associated computational model. The proposed approach consists of sequentially carrying out NSGA-II while using an adaptively built Kriging model to estimate of the quantiles. Finally, the methodology is adapted to account for mixed categoricalcontinuous parameters as the applications involve the selection of qualitative design parameters as well. The methodology is first applied to two analytical examples showing its efficiency. The third application relates to the selection of optimal renovation scenarios of a building considering both its life cycle cost and environmental impact. It shows that when it comes to renovation, the heating system replacement should be the priority.

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Section: Example 3: Application To Building Renovationmentioning

confidence: 99%

Section: Example 3: Application To Building Renovationmentioning

confidence: 99%

Multi-objective robust optimization using adaptive surrogate models for problems with mixed continuous-categorical parameters

Moustapha,

Galimshina,

Habert

et al. 2022

Preprint

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show abstract

“…They conclude that the calibration of building models reduces the uncertainty of model predictions. Galimshina et al (2020) employ statistical methods to find robust building retrofit decisions. They show significant differences in robustness between life cycle assessment (LCA) and life cycle cost (LCC).…”

Section: Uncertainty In Building Performance Energymentioning

confidence: 99%

Impact of Measurement Uncertainty on Building Modeling and Retrofitting Decisions

Frei

Hischier

Deb

et al. 2021

Front. Built Environ.

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Retrofitting buildings is essential for improving the existing global building stock. Innovations in wireless sensor networks have provided new means for scalable and potentially low-cost solutions for evaluating optimal retrofit measures in a building. Building models are used to explore different retrofit options and to find effective combinations of retrofit measures for a building in question. This paper departs outlining a novel grey-box modeling process for building retrofit based on measurement data. However, it is unknown if the measurement data and, as a consequence, the retrofit analysis is affected by uncertainties due to measurement accuracy and other factors. Quantifying these uncertainties during the analysis process is important in the context of making effective retrofit decisions. Consequently, this work examines the influence of measurement uncertainties on the generation of the retrofit models and the suggested retrofit measures. The results illustrate that measurement uncertainty is manageable for retrofit decisions, i.e., the measurement uncertainties rarely influence the ranking of retrofit measures. However, reduced measurement uncertainties are beneficial for adequately sizing the building retrofit interventions. It is shown that measurement uncertainty of flow meter measurements and indoor temperature measurements have the biggest impact on the heat loss coefficient estimation error, which ranges overall from 3 to 26%. Further, it is shown that some retrofit measures are more sensitive to uncertainty in the input data, such as district heating and wood pellets boilers, compared to measures that include heat pumps.

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“…Searching for optimal renovation scenarios, the difference between environmentally and financially optimal renovation scenarios is found based on life cycle assessment [19]. But [20] have found that changing only the energy system is more beneficial than performing complete renovation of the building. These authors highlight the need for new approaches in building renovations.…”

Section: Introductionmentioning

confidence: 99%

System Dynamics Modeling for the Transition Trom Traditional Historic Urban Block to a Positive Energy Block

Andra¹,

Blumberga²,

Vanaga³

et al. 2022

Preprint

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Statistical method to identify robust building renovation choices for environmental and economic performance

Cited by 39 publications

References 50 publications

Multi-objective robust optimization using adaptive surrogate models for problems with mixed continuous-categorical parameters

Multi-objective robust optimization using adaptive surrogate models for problems with mixed continuous-categorical parameters

Impact of Measurement Uncertainty on Building Modeling and Retrofitting Decisions

System Dynamics Modeling for the Transition Trom Traditional Historic Urban Block to a Positive Energy Block

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