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Uncertainty analysis of life cycle assessment input parameters on city quarter level
... This data scarcity has motivated the ongoing development of Geographic Information System (GIS)-based district-scale LCA approaches such as ubem.io (Ang et al. 2021), Teco (Schildt et al. 2022), and urbiþ (Harter et al. 2021). The framework ubem.io ...
... However, neither tool is currently able to accurately represent interior walls in terms of mass (LCI) and environmental implications (LCIA). It is due to either interior walls being out of the LCA scope or a flawed estimation method for their geometrical, thermo-physical, and environmental properties (Harter et al. 2021;Schildt et al. 2022). ...
... This raises the question of the appropriate degree of accuracy for building archetypes in district-scale LCA. Previous work has emphasised the significance of a building's construction type, and utility configuration (Harter et al. (2021)). There is an ongoing development of tools to heuristically determine the Life Cycle Inventory (LCI) and Life Cycle Impact Assessment (LCIA) of buildings and districts, employing data based on Geographical Information Systems (GIS), involving heat load simulations. ...
... There is an ongoing development of tools to heuristically determine the Life Cycle Inventory (LCI) and Life Cycle Impact Assessment (LCIA) of buildings and districts, employing data based on Geographical Information Systems (GIS), involving heat load simulations. These tools include urbi+ (Harter et al. (2021)), ubem.io (Ang et al. (2021)), and Teco (Schildt et al. (2022)). ...
... The case study introduces the district-scale functional units of "Carbon footprint reduction potential ( kt CO2eq ha·y )" and "Carbon footprint intensity reduction potential ( kg CO2eq m 2 ·y )", and emphasises the lack of urban-scale data on building materials and service life, refurbishment rates, and the operational stage to increase the accuracy of their results. This is stressed by studies where LCA uncertainty analyses yielded the chosen building material type, the average service life, the share of renewable primary energy sources, and the energy distribution system as the most significant variables [86,87]. GIS data is being increasingly used for the sustainability assessment of districts [3,88,89]. ...
Climate neutrality goals in the building sector require a large-scale estimation of environmental impacts for various stakeholders. Life Cycle Assessment (LCA) is a viable method for this purpose. However, its high granularity, and subsequent data requirements and effort, hinder its propagation, and potential employment of Machine Learning (ML) applications on a larger scale. The presented paper outlines the current state of research and practice on district-scale building LCA in terms of standards, software and certifications, and data availability. For this matter, the authors present the development and application of two district-scale LCA tools, Teco and DisteLCA, to determine the Global Warming Potential (GWP) of three different residential districts. Both tools employ data based on (including, but not limited to) CityGML, TABULA, and ÖKOBAUDAT. The results indicate that DisteLCA’s granular approach leads to an overestimation of environmental impacts, which can be derived from the statistical approach to operational energy use and related emissions. While both tools lead to substantial time savings, Teco requires less manual effort. The linkage of the aforementioned data sources has proven laborious and could be alleviated with a common data framework. Furthermore, large-scale data analysis could substantially increase the viability of the presented approach.
With current efforts to increase energy efficiency and reduce greenhouse gas (GHG) emissions of buildings in the operational phase, the share of embedded energy (EE) and embedded GHG emissions is increasing. In early design stages, chances to influence these factors in a positive way are greatest, but very little and vague information about the future building is available. Therefore, this study introduces a building information modeling (BIM)-based method to analyze the contribution of the main functional parts of buildings to find embedded energy demand and GHG emission reduction potentials. At the same time, a sensitivity analysis shows the variance in results due to the uncertainties inherent in early design to avoid misleadingly precise results. The sensitivity analysis provides guidance to the design team as to where to strategically reduce uncertainties in order to increase precision of the overall results. A case study shows that the variability and sensitivity of the results differ between environmental indicators and construction types (wood or concrete). The case study contribution analysis reveals that the building’s structure is the main contributor of roughly half of total GHG emissions if the main structural material is reinforced concrete. Exchanging reinforced concrete for a wood structure reduces total GHG emissions by 25%, with GHG emissions of the structure contributing 33% and windows 30%. Variability can be reduced systematically by first reducing vagueness in geometrical and technical specifications and subsequently in the amount of interior walls. The study shows how a simplified and fast BIM-based calculation provides valuable guidance in early design stages.
Though sensitivity analysis has been widely applied in the context of building energy models (BEMs), there are few studies that investigate the performance of different sensitivity analysis methods: in relation to dynamic, high-order, non-linear behaviour and the level of uncertainty in building energy models. We scrutinise three distinctive sensitivity analysis methods: (a) the computationally efficient Morris method for parameter screening, (b) linear regression analysis (medium computational costs) and (c) Sobol method (high computational costs). It is revealed that the results from Morris method taking the commonly used measure for parameter influence can be unstable, while using the median value yields robust results for evaluations with small sample sizes. For the dominant parameters the results from all three sensitivity analysis methods: are in very good agreement. Regarding the evaluation of parameter ranking or the differentiation of influential and negligible parameters the computationally costly quantitative methods: provide the same information for the model in this study as the computational efficient Morris method using the median value. Exploring different methods: to investigate higher-order effects and parameter interactions, reveals that correlation of elementary effects and parameter values in Morris method can also provide basic information about parameter interactions.
CityGML is an open data model and XML-based format for the representation and exchange of virtual 3D city models. It is based
on the Geography Markup Language version 3.1.1 (GML3). Both CityGML and GML3 are international standards issued by the Open
Geospatial Consortium (OGC). CityGML not only represents the shape and graphical appearance of city models but specifically
addresses the object semantics and the representation of the thematic properties, taxonomies and aggregations. The paper gives
an overview about CityGML, its modelling aspects and design decisions, recent applications, and its relation to other 3D standards
like IFC, X3D, and KML.
In 1991 Morris proposed an effective screening sensitivity measure to identify the few important factors in models with many factors. The method is based on computing for each input a number of incremental ratios, namely elementary effects, which are then averaged to assess the overall importance of the input. Despite its value, the method is still rarely used and instead local analyses varying one factor at a time around a baseline point are usually employed.In this piece of work we propose a revised version of the elementary effects method, improved in terms of both the definition of the measure and the sampling strategy. In the present form the method shares many of the positive qualities of the variance-based techniques, having the advantage of a lower computational cost, as demonstrated by the analytical examples.The method is employed to assess the sensitivity of a chemical reaction model for dimethylsulphide (DMS), a gas involved in climate change. Results of the sensitivity analysis open up the ground for model reconsideration: some model components may need a more thorough modelling effort while some others may need to be simplified.
The concept of multi-Level-of-Development (multi-LOD) modelling represents a flexible approach of information
management and compilation in building information modelling (BIM) on a set of consistent levels. From an
energy perspective during early architectural design, the refinement of design parameters by addition of information
allows a more precise prediction of building performance. The need for energy-efficient buildings
requires a designer to focus on the parameters in order of their ability to reduce uncertainty in energy performance
to prioritise energy relevant decisions. However, there is no method for assigning and prioritising information
for a particular level of multi-LOD. In this study, we performed a sensitivity analysis of energy models
to estimate the uncertainty caused by the design parameters in energy prediction. This study allows to rank the
design parameters in order of their influence on the energy prediction and determine the information required at
each level of multi-LOD approach. We have studied the parametric energy model of different building shapes
representing architectural design variation at the early design stage. A variance-based sensitivity analysis
method is used to calculate the uncertainty contribution of each design parameter. The three levels in the
uncertainty contribution by the group of parameters are identified which form the basis of information required
at each level of multi-LOD BIM approach. The first level includes geometrical parameters, the second level
includes technical specification and operational design parameters, and the third level includes window construction
and system efficiency parameters. These findings will be specifically useful in the development of a
multi-LOD approach to prioritise performance relevant decisions at early design phases.
During building design and especially in early stages, important decisions influencing the lifecycle-based energy demand of buildings are made. Life Cycle Energy Assessment (LCEA) is used to evaluate this energy demand already in early stages of design. However, at that point, the building design and the related information can quickly change and are subject to potentially large uncertainty. This uncertainty in building information influences the LCEA and therefore decisions taken by the designer. Due to the uncertainty, it is difficult to distinguish between the performance of different design variants to decide for the best option. This study presents a method to perform LCEA and to assess and consequently strategically reduce the influence of uncertainties in buildings’ information on the LCEA in early design stages. Uncertainty analysis is used to assess the influence of uncertainty on LCEA and to prioritize decisions to reduce uncertainty. The method is embedded in a multi-Level of Development (LOD) modelling approach covering the development of the building during the early stages of design. The method is applied to seven different building shapes as a proof of concept. It is concluded that the method renders valid results to assess the project-specific uncertainty in LCEA results.
This review summarizes and organizes the literature on life cycle assessment (LCA), life cycle energy analysis (LCEA) and life cycle cost analysis (LCCA) studies carried out for environmental evaluation of buildings and building related industry and sector (including construction products, construction systems, buildings, and civil engineering constructions). The review shows that most LCA and LCEA are carried out in what is shown as "exemplary buildings", that is, buildings that have been designed and constructed as low energy buildings, but there are very few studies on "traditional buildings", that is, buildings such as those mostly found in our cities. Similarly, most studies are carried out in urban areas, while rural areas are not well represented in the literature. Finally, studies are not equally distributed around the world.
This paper provides a review on three streams of life cycle studies that have been frequently applied to evaluate the environmental impacts of building construction with a major focus on whether they can be used for decision making. The three streams are Life Cycle Assessment (LCA), Life Cycle Energy Assessment (LCEA) and Life Cycle Carbon Emissions Assessment (LCCO2A). They were compared against their evaluation objectives, methodologies, and findings. Although they share similar objectives in evaluating the environmental impacts over the life cycle of building construction, they show some differences in the major focuses of evaluation and methodologies employed. Generally, it has been revealed that quite consistent results can be derived from the three streams with regard to the relative contribution of different phases of life cycle. However, discrepancies occur among the findings obtained from the three streams when different compositions of fuel mixes are used in power generation, or when the overall impacts are not contributed mostly by greenhouse gases emissions. The use of different functional units in different studies also makes it difficult to compare results with benchmarks or results from previous studies. Besides, there are drawbacks in boundary scoping, methodology framework, data inventory and practices which impair their usefulness as a decision making support tool for sustainable building designs.
This article deals with global quantitative sensitivity analysis of the Level E model, a computer code used in safety assessment for nuclear waste disposal. The Level E code has been the subject of two international benchmarks of risk assessment codes and Monte Carlo methods and is well known in the literature. We discuss the Level E model with reference to two different settings. In the first setting, the objective is to find the input factor that drives most of the output variance. In the second setting, we strive to achieve a preestablished reduction in the variance of the model output by fixing the smallest number of factors. The emphasis of this work is on how to define the concept of importance in an unambiguous way and how to assess it in the simultaneous occurrence of correlated input factors and non-additive models.
Variance based methods have assessed themselves as versatile and effective among the various available techniques for sensitivity analysis of model output. Practitioners can in principle describe the sensitivity pattern of a model Y=f(X1,X2,…,Xk) with k uncertain input factors via a full decomposition of the variance V of Y into terms depending on the factors and their interactions. More often practitioners are satisfied with computing just k first order effects and k total effects, the latter describing synthetically interactions among input factors. In sensitivity analysis a key concern is the computational cost of the analysis, defined in terms of number of evaluations of f(X1,X2,…,Xk) needed to complete the analysis, as f(X1,X2,…,Xk) is often in the form of a numerical model which may take long processing time. While the computational cost is relatively cheap and weakly dependent on k for estimating first order effects, it remains expensive and strictly k-dependent for total effect indices. In the present note we compare existing and new practices for this index and offer recommendations on which to use.
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Proceedings of the 17th IBPSA Conference
Bruges, Belgium, Sept. 1-3, 2021
1887
https://doi.org/10.26868/25222708.2021.30672