Determining factors

The data required for the determining factors can be divided into three categories: - Climate data: The input of climate data depends on how the operational energy

demand will be calculated, as described in Section 1.2.2.4. For both QSSM and DBPS methods, the respective climate data is loaded into the model.

- User data: Here, user data from DIN V 18599-10:2011 is employed and loaded into the model. Any other user data could be similarly integrated. An experienced user can also numerically input their own user data and adjust it parametrically.

- Reference study period: The reference study period (RSP) is entered numerically, depending on the scope of the LCA. The parametric input allows for the quick com- parison of different RSPs.

3.1.2. Calculation

The approach presented here combines the primary energy demand and environmental impact of the building in the term impact. It distinguishes between the operational impact (IO) resulting from the operational energy use of the building (module B6) and the embodied

impact (IE) resulting from production and the EOL of the building (modules A1-A3, C3, C4,

and D). The replacement of building components (module B4) is also considered as IE. The

life cycle impact (ILC) is the sum of IE and IO (see Equation 1). While this is a general formula,

only the life cycle modules indicated in Table 22 are integrated in the calculation in this thesis.

(1)

3.1.2.1. Operational impact

First of all, the energy demand in the use phase has to be known for the calculation of the operational impact (IO). PLCA differentiates between energy demand influenced by the

building design and energy demand mostly influenced by the user. The first kind is calculated specifically for each individual design, while the latter kind is integrated using statistical data based on user profiles. The design-influenced energy demand can be calculated either using QSSM or DBPS. This option is provided, because both approaches have their advantages and disadvantages, as shown in Section 1.2.2.2. In both cases a thermal model is needed, which is automatically extracted from the geometric model. As described in Section 1.2.2.2, QSSM is much more time-efficient for optimization based on many design variants. Therefore, the parametric energy demand calculation based on DIN V 18599-2:2011 (Lichtenheld et al. 2015, pp.1-3) is employed in the early design stages where possible.

IO consists of the sum of all different kinds of operational energy demand during the use

phase (EDi) divided by the performance factor (PFi) for the specific building services and

multiplied by the operational impact factor of the energy carrier (IFO,i) (see Equation 2). ED

refers to the useful energy demand and is calculated with reference to one year of opera- tion. Therefore, the sum is multiplied by the number of years of the reference study period (RSP). The PF is introduced to describe different types of building services with one system- atic method. It depends on the performance of the building service employed, such as the annual performance factor (APF) for a heat pump or the efficiency for a gas-condensing boiler, and includes all different kinds of losses within the building. The greater the PF, the

lower the resulting IO. ED divided by PF equals the amount of final energy which enters the

building. The operational impact factor (IFO) is imported from the combined database. It

depends on the energy carrier employed and the indicators chosen for the LCA. For primary energy it is also called the primary energy factor. For example, the PENRT equals 8.775 MJ for 1 kWh of electricity in the European mix in the year 2008.

3.1.2.2. Embodied impact

The embodied impact (IE) is calculated by multiplying the mass of each material (Mj) by the

specific embodied impact factor of the material (IFE,j) (see Equation 3). To determine the

mass, first of all, the areas of the different building surfaces have to be calculated. The surface areas are then multiplied by the thickness and density of the specific material. The density is imported from the combined database, together with the RSL and the specific IFE.

In this way, the IE of every component is calculated and summed up to obtain the IE of the

entire building.

(3)

If the RSL of a building component (RSLj) is lower than the RSP of the building, the necessary

number of replacements (Rj) is considered (see Equation 4). For example, if a coating

possesses a RSL of 20 years, it has to be renewed twice within an RSP of 50 years, so R equals 2.

The impact factors (IFO,i, IFE,j) depend on the indicator chosen for the LCA. If more than one

indicator is used, the impact factors are written as vectors of the indicators applied. In consequence, the resulting impact (IO, IE) is a vector as well. The advantage of using vectors

for the impact factors is that the indicators chosen for evaluation can be easily modified depending on the available data. Equation 5 shows IFO,i and IFE,j for the eight indicators used

(Ökobilanzdaten im Baubereich) provided by KBOB70 _{for example, only three indicators,}

namely PENRT, GWP, and UBP are available, resulting in a vector with three entries.

, ₍₅₎

All terms of the equations are assumed to be static, although some values might change in the future, for example the PF of the building services. Furthermore, the electricity mix will change, and as a result the environmental data of the material will also change. Replaced building components will then have a lower embodied impact. These considerations are neglected here, but they could be integrated into the equations in future, leading to a dynamic LCA.

3.1.3. Output

The aim is to provide the architects with insight into the environmental impact of their design and indicate potential for improvement. Therefore, it is not only important to output numerical results, but also to display the results graphically in an easily comprehensible manner for non-LCA-experts. In addition, the results can be exported to spreadsheets for further use, such as for building certification.

3.1.3.1. Numerical Results

The results of the ILC are reported according to the vectors of the impact factors (IFO,i, IFE,j). In

addition to the final results of the LCA, partial results, such as IO for heating or the IE of

windows, can be output separately. As such, very high values in partial results can indicate potential for improvement. In addition, the results and the calculation method are made transparent.

70 _{The dataset Ökobilanzdaten im Baubereich with explanations can be downloaded at http://www.eco-}

bau.ch/resources/uploads/Oekobilanzdaten/kbob-Oekobilanzdaten-Empfehlung_29_07_2014.pdf (accessed

In general, the results should be presented in a way that is understandable for users that do not have detailed knowledge of LCA. Often, absolute results are not meaningful to non- experts: for example, a client is probably unable to interpret the statement “your building design has an acidification potential of 0.3 kg SO2-equivalent/m² a”. A more promising

approach is to use the results of the LCA to compare different design variants. It is far easier to communicate that design A possesses 3.7 t CO2-equivalent less GWP than designs B and C

while providing the same function. The client can then make an informed decision taking other parameters into consideration, such as costs.

Normalization, weighting, and aggregation of several indicators into a single score is also possible. The parametric approach allows the advanced user to define and adapt their own weighting factors in order to take the specific goals of the LCA study into consideration. Furthermore, it allows different predefined weighting factors to be employed, such as those of a particular building certification system. In this case, it allows architects to optimize the building design to score the most points in the certification system. This is illustrated using a new residential building and DGNB certification as an example in Section 4.2. In addition to the aggregated and weighted results, the individual midpoint indicators are reported separately to conform to ISO 14040:2009.

3.1.3.2. Visualization

Architects are accustomed to working on the basis of visual information. Therefore, graphical representation of the LCA results is very important. The means for displaying the results are not a key part of this thesis and would go beyond its scope, but some examples of represen- tation are used in the examples in Chapter 4. These consist of different bar and pie charts to indicate potential for improvement.

3.1.4. Optimization

As noted in Section 1.3.4, architects have two options for optimizing a building design. They can either manually vary parameters and improve the design iteratively, or apply computa- tional optimizers. As Figure 22 indicates, the parametric model provides both options for improving a design for minimum ILC.

For the computational approach, evolutionary algorithms (EA) are chosen because of their suitability for problems with little background knowledge. To optimize for more than one objective and find the trade-off between conflicting objectives, evolutionary multi-criteria optimizers (EMO) can be employed. The visualization of the Pareto front can be a valuable means to provide a basis for deciding on conflicting objectives. This is shown in Section 4.1.5 for the example of the trade-off between GWPLC and investment costs.

As described in Section 2.4.1, both approaches have their advantages and disadvantages. To make use of the advantages of both approaches, a combination is proposed. Computational optimizers are employed for certain decisions where the objective can be clearly defined and the influence on other criteria is negligible, e.g. the optimization of the thickness of an insulation material. Parameters with a large influence on other criteria, such as appearance or functionality, are varied manually. The window layout, for example, significantly influ- ences functional criteria such as daylight availability, views to the outside, and the appear- ance of the whole building. In this case a manual variation might lead to solutions that satisfy architects and clients faster and reduce the environmental impact while preserving those qualities. The detailed development of such a semi-automated method would go beyond the scope of this thesis, but the application of a semi-automatic approach is briefly illustrated in Section 4.2.

3.1.5. Summary of Section 3.1

What is the key element of the parametric method and how is the input parametrized? The key element of the proposed method called Parametric Life Cycle Assessment (PLCA) is a digital, parametric LCA model. The workflow involved in employing the model can be divided into four steps: input, calculation, output, and optimization. The unique and most important characteristic of the model is that all four steps and all components within those steps are interlinked and form a closed calculation loop. This provides the basis for optimization. The basic prerequisite for the parametric model is the parametrization of all input. To structure the input, it is divided into three categories: geometric information, building

materials and services, and determining factors. To input the geometry, either a simple 3D

model can be used, from which the necessary parameters are extracted automatically and transferred to the LCA model, or the geometry can be directly modelled using parametric design software. To input building materials and services, three kinds of data are necessary: environmental data, RSL data, and physical properties. To simplify the input, a combined database is established. Environmental data is based on ökobau.dat, typical RSL data are exported from the eLCA online tool, and physical properties like conductivity are taken from DIN 4108-4:2013. To further simplify the input for screening LCA, a component catalogue based on this combined dataset is provided. Determining factors, such as climate or user data, are taken from standards and employed by the model. The RSP is also defined

parametrically. All necessary input is parametrized and can quickly be varied for optimization purposes, either manually by the architect or by a computational optimizer.

Which algorithms were developed to calculate the LCA?

A distinction has been made between operational and embodied impact (IO and IE). Both are

calculated separately but simultaneously and then added together to provide the life cycle

impact (ILC). The operational impact consists of the sum of all different types of energy

demand during the use phase divided by a performance factor for the specific building services, multiplied by the impact factor of the energy carrier, and multiplied by the number of years of the reference study period (RSP). Within this step, the energy demand is directly calculated using QSSM or DBPS to avoid the exporting and importing that is necessary for conventional approaches. The embodied impact of one material is calculated by multiplying the mass by the specific impact factor of the material and by the number of replacements. In this way, the embodied impact of every component is calculated and summed up to

generate an embodied impact for the complete building.

What allows for an easily comprehensible output of the LCA results?

To provide architects with insight into the environmental impact of their design and indicate potential for improvement, partial results are output in addition to the overall results. Different graphical representations of the LCA results can be output in addition to the numerical output to provide an easily comprehensible means for non-LCA-experts. Further- more, the results can be exported to spreadsheets for further use, such as for building certification.

Which approaches to optimize the building design does the method provide?

The parametric model provides both possibilities for optimizing a design for minimum life cycle impact: manual variation of parameters by the architect or application of computation- al optimizers. The approaches can also be combined to make use of the advantages of both approaches. Computational optimizers are employed for certain decisions with a clear objective, while other parameters with a large influence on qualitative criteria are varied manually.