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Energy, Ecology and Environment

, Volume 3, Issue 1, pp 13–23 | Cite as

Parametric real-time energy analysis in early design stages: a method for residential buildings in Germany

  • Alexander Hollberg
  • Thomas Lichtenheld
  • Norman Klüber
  • Jürgen Ruth
Original Article
First Online:
Received:
Revised:
Accepted:

Abstract

The greatest potential for optimizing the energy efficiency of buildings is in the early design stages. However, in most planning processes energy analysis is conducted shortly before construction when major changes to the design have a high cost impact. The integration of energy performance analysis in the early design stages is therefore highly desirable, but requires suitable tools able to quickly generate results that can help the planner optimize the building design. Parametric design approaches permit the effortless generation of many variants and therefore represent a suitable way of testing different alternatives in the early design stages. Most plug-ins for parametric design software currently rely on dynamic building performance simulation which provides detailed results, but requires computation times ranging from 20 s to 5 min. As optimization processes typically require several thousand simulations, the computation time can quickly amount to days. The approach presented in this paper proposes a real-time energy demand calculation based on a quasi-steady state method defined by the German standard DIN V 18599 which defines the national implementation of the European Directive on the Energy Performance of Buildings. The results are verified of tests on three residential reference buildings in Germany in comparison with an accredited commercial software product. An application example indicates the great potential for easy-to-use energy optimization in the early design stages.

Keywords

Real time Energy demand calculation Early design stage Parametric design Building design optimization Architecture 

List of symbols

Qsink

Heat sinks

Qsource

Heat sources

QS

Solar heat gains

Qsol,trans

Solar heat gains from transparent surfaces

QI

Internal thermal gains (heat or cold)

Qh,b

Balanced energy need for heating of the building zone

QT

Transmission heat sinks

QV

Ventilation heat sinks

ΔQc,b

Heat stored in building components which is emitted in periods of reduced operation

η

Utilization factor

FX

Temperature correction factor

Θe

External air temperature

Θi

Reference internal temperature

Θi,h,soll

Internal set-point temperature for heating during normal heating periods

Θi,h

Internal temperature for reduced weekend operation

Θu

Mean temperature in unheated space

ΔΘEMS

Consideration of energy management system

ΔΘi,NA

Permitted reduction in the internal temperature periods of reduced heating during the night

Cwirk

Effective heat capacity

fNA

Correction factor for reduced heating mode during the night

tNA

Daily operation hours with reduced heating mode

τ

Thermal time constant of a building zone

atb

Ratio of indirectly heated areas to the total area

FX

Temperature correction factor

AG

Area of floor slab

P

Perimeter of floor slab

1 Introduction

1.1 Background

Most European countries have implemented national regulations for building energy performance which comply with the demands of the European Directive on the Energy Performance of Buildings (EU 2010). In Germany, the Energy Saving Ordinance (EnEV) (Bundesregierung 2013) stipulates a monthly energy balance using a quasi-steady state method. The algorithms for calculating the energy demand are defined in DIN V 18599 (DIN 2011). For residential buildings, the older DIN V 4108 (DIN 2003) can at present still be used. Dynamic building performance simulation is only permitted for proof of protection against overheating in summer.

Decisions made in the early phases of the design have significant consequences as they lay down general conditions for the subsequent planning process (see Fig. 1). As such, they also have the biggest impact on energy demand (Hegger et al. 2007). Consequently, optimizing a design for energy efficiency is best achieved in the early design stages (stages 1 and 2).
Fig. 1

Stages in the architectural design process, based on Paulson Jr. (1976)

The most fundamental decisions, such as the building form, orientation and window layout, are often made by designers in the early design stages, with little or no support from simulation software (Picco et al. 2014). In recent years, various incentives have been started to shift the performance analysis of buildings from the detailed planning into early design stages. Most of them propose the application of dynamic building simulation, e.g. Morbitzer et al. (2001), Petersen and Svendsen (2010), Negendahl (2015). Dynamic building simulation tools facilitate the modelling of complex systems and their interrelationships, but they have not yet become recognized as design support tools by building design teams to the same extent as design software (Morbitzer et al., 2001). There are currently a number of obstacles to energy optimizing the building design within the planning process and especially in the early designs stages:

  • Many boundary conditions need to be defined, but only limited information is available about the respective building in the early design stages.

  • Inputting information on these boundary conditions requires extensive background knowledge, making it difficult for non-experts to apply building simulation during planning.

  • The simulation is computationally intensive. Depending on the size of the building, the simulation can take between 20 s and 5 min on a standard PC. While this may be acceptable for a single simulation, the time required for simulating many variants, as needed for optimization, quickly multiplies to many hours or even several days—clearly too long for the early design stages.

Carlos and Nepomuceno (2012) describe the difficulties in applying dynamic simulation in the early design stages. Furthermore, they show that for moderate climates, quasi-steady state approaches can produce valid results. According to van Dijk et al. (2006), the monthly balancing of the simplified method is well suited for continuously heated buildings in warm, moderate and cold European climates. This would therefore be adequate for residential buildings which are mostly continuously heated. The time and effort required for dynamic simulations are such that the additional cost of these planning services often exceeds the budget for common residential buildings. Residential buildings, however, account for 75% of the floor area in Europe (Economidou et al. 2011). There is therefore a corresponding demand for simplified methods.

The algorithms used in quasi-steady state methods are simple enough for tools to be able to output the results in real time. Current commercial tools based on DIN V 18599 usually require the numerical input of areas in tabular form, but manual input quickly becomes very time-consuming when comparing more than one variant. Consequently, users often stop after calculating a few design variants. The full optimization potential of is therefore not exploited.

1.2 Parametric design and existing tools

Parametric design has existed for a long time, but only recently gained popularity in architecture and design through the availability of corresponding computer tools (Davis, 2013). Standard CAD software is used to draw geometric forms, emulating the way designers draw on paper. The geometry is fixed, and subsequent changes require the initial geometry to be redrawn. Parametric design approaches use mathematical formulae to describe the geometry: the form is defined by parameters, such as the width, height and length of a cube (see Fig. 2). These parameters can be modified easily, allowing for a quickly variation of the basic form. In addition to the building geometry, other defining parameters, e.g. material properties or climate data, can also be defined and changed. Furthermore, the parametric definition makes it possible for the computer to automatically generate variants—and this can serve as the basis of an optimization process.
Fig. 2

Parametric definition of a cube with number sliders

A variety of parametric tools are available for 3D CAD programs: Grasshopper3D (GH) is a parametric tool with a graphical algorithm editor based on Rhinoceros (Rhino). Developed by David Rutten, it was first published in 2007. Since then, a large number of third party plug-ins and links to other software have been developed. For building performance simulation, a connection to EnergyPlus is provided by plug-ins such as Archsim (Dogan 2015), Honeybee (Roudsari et al. 2013) or Diva (Jakubiec and Reinhart 2011). Archsim and TRNSYS-Lizard (Frenzel and Hiller 2014) also provide an interface for TRNSYS. Currently, the only plug-in for a quasi-steady state method is based on ISO 13970 and connects GH to an Excel-based balancing software called Energy Performance Calculator (Ahuja et al. 2015). All these plug-ins export data from the parametric design software for external analysis and then re-import it for visualization. But exporting also introduces a delay and can be the cause of errors. Trials using several of these tools in student design projects showed that the process of exporting and importing acted as a barrier, even when the results are reported a few seconds later (Hollberg et al. 2016). Architects and engineers expect to have instant feedback on their designs, and they ask for a visualization of the results (Attia et al. 2013).

The main objective of this paper is the implementation of a quasi-steady state method based on DIN V 18599 in GH in order to enable parametrically controlled energy balancing in real time. The algorithms are programmed directly in GH, obviating the need to export information. The results can be displayed on a second screen parallel to the design screen, providing an instant visualization of the effects of changes to the design on the energy demand. In this way, the results can be directly used as design decision support. Furthermore, the parametric approach provides a basis for time efficiently optimizing the building design. The geometry can either be defined in GH or be drawn conventionally in Rhino for planners unfamiliar with parametric software.

2 Implementation of a quasi-steady state method in parametric software

The implementation of a quasi-steady state method in GH is carried out in three stages. First, the geometry and all necessary boundary conditions are defined. In a second stage, the algorithms of DIN V 18599 are implemented following a modular approach. The results of the individual modules are combined to monthly energy balances. In the third stage, the results are output. The structure of the tool also consists of three steps, namely input, calculation and output (see Fig. 3).
Fig. 3

Structure of the parametric tool

2.1 Input

In general, the geometry can either be built directly in GH or drawn in Rhino and then transferred automatically to GH. The model only consists of simple surfaces, i.e. the components of the building skin, such as walls, are constructed as 2D surfaces instead of 3D solids. The thickness of the components is added when the materials are defined. When drawing the geometry in Rhino, a separate layer is predefined for each component with a different boundary condition, e.g. wall to exterior, wall to unconditioned zone or wall to ground. Thirteen different possible layers result (see Fig. 4). The individual surfaces are drawn on the respective layers, which automatically assign the right boundary conditions. The colour coding gives the user instant visual feedback and makes it possible to verify that.
Fig. 4

Layers with colour codes for defining boundary conditions. *F x-value depends on geometric characteristics, see Sect. 2.2

After assigning each building component to a specific category using the layers, a construction set-up is assigned. To simplify the input, each construction set-up consists of a certain number of predefined functional layers. The exterior wall, for example, consists of four layers: exterior cladding, insulation, primary construction and interior lining. The material and thickness of each functional layer are predefined and can be adapted by the user. These definitions are based on the reference u-values in the EnEV required to achieve an adequate level of energy performance. This allows the planner to receive feedback on the geometry of the design before making any choices on material. The material can then be chosen from a dropdown list. For certain layers, such as insulation, the thickness can be varied using a slider (see Fig. 5). In this way, a large number of variants can be easily generated. Additionally, the user can also add own materials. Depending on the layer, the tool automatically chooses the inner and outer heat transfer resistance according to DIN EN ISO 6946 (DIN 2008). The thermal resistance of the component is calculated by dividing the material’s thickness by the stored conductivity, and finally, the u-value is calculated.
Fig. 5

Example of parametric material definition in GH

The climate of the site is defined by choosing one of the 15 different climatic regions in Germany as given in DIN V 18599-10. Data for a test reference year of 2010 are provided for each of these regions. Choosing the climatic region loads all the climate data, such as average temperatures or solar irradiation, into GH. The predefined climate is Potsdam, which is the reference climate according to EnEV.

The tool has currently only been developed for residential buildings. Depending on whether the building to be analysed is a single or a multi-family house, different user data are loaded from DIN V 18599-10 into GH. User data consist of heating set points, operating hours and internal loads, amongst others.

2.2 Calculation

In its current form, the tool implements the calculation of heat demand, which is most relevant for residential buildings in moderate climates. The calculation of cooling demand will follow in a later version. In the long term, a simplified approach for accounting HVAC systems could also be integrated.

In the following, the method given in DIN V 18599-2 is briefly explained using equations from the DIN standards. All heat sources (Q source) and sinks (Q sink) are balanced on a monthly basis. For Q source, a monthly utilization factor (η) is introduced to consider the fact that not all heat gains are useful, e.g. in summer. η depends on the monthly relation of sources to sinks and a thermal time constant of the building consisting of the effective heat storage capacity and heat transfer through transmission and ventilation. The monthly heating demand results as balance of sources and sinks (see Eq. 1). Here, only residential buildings are considered; as such, a correction for intermittent usage is excluded (ΔQ c,b = 0)
$$ \varvec{Q}_{\text{h,b}} = \varvec{Q}_{\text{sink}} -\varvec{\eta}*\varvec{Q}_{\text{source}} - \Delta \varvec{Q}_{\text{c,b}} $$
(1)
Q sink consists of multiple summands (see Eq. 2). In addition to transmission heat sinks (Q T) and ventilation heat sinks (Q V), heat loss through long-wave radiation of the buildings surfaces (Q S) is also considered. Because cooling (Q C,sink) and intermittent usage, such as reduction on weekends, (Q Is,sink) are not being considered at present, these terms are set to zero.
$$ \varvec{Q}_{\text{sink}} = \varvec{Q}_{\text{T}} + \varvec{Q}_{\text{V}} + \varvec{Q}_{\text{Is,sink}} + \varvec{Q}_{\text{S}} + \varvec{Q}_{\text{C,sink}} $$
(2)
Similarly, all heat sources are added up to Q source (see Eq. 3). Q S includes all solar gains through transparent and opaque building components. Q I,source represents all internal gains from people, lighting and appliances. This also includes unregulated heat entry from building service systems, which can be set to zero, here, according to DIN V 18599-2. Q T and Q V stand for heat sources from transmission and ventilation. These might come from neighbouring zones with higher temperature set points or from outside if the temperature outside happens to be higher than inside.
$$ \varvec{Q}_{\text{source}} = \varvec{Q}_{\text{S}} + \varvec{Q}_{\text{T}} + \varvec{Q}_{\text{V}} + \varvec{Q}_{\text{I,source}} $$
(3)
According to the EnEV, diurnal temperature setback (i.e. at night) also has to be considered. The effect is taken into account through an adaption of the monthly internal balance temperature (Θi,h) (see Eq. 4). Here, the building automation ∆ΘEMS is set to zero.
$$ \varvec{\varTheta}_{\text{i,h}} = { \hbox{max} }\left( {\varvec{\varTheta}_{\text{i,h,soll}} + \Delta\varvec{\varTheta}_{\text{EMS}} - \varvec{f}_{{\varvec{NA}}} \left( {\varvec{\varTheta}_{\text{i,h,soll}} -\varvec{\varTheta}_{\varvec{e}} } \right);\varvec{\varTheta}_{\text{i,h,soll}} - \Delta\varvec{\varTheta}_{\text{i,NA}} \frac{{\varvec{t}_{\text{NA}} }}{{24\varvec{h}}}} \right) $$
(4)
The monthly external balance temperature (Θe) is given in DIN V 18599-10 for 15 German climatic regions. For building components that border unheated zones or the ground, but not the outside air, a reduced difference of balance temperatures (Θi − Θe) applies. In order to take this into account, a simplified approach to determining a mean balance temperature (Θu) is employed using a temperature correction factor (FX) (see Eq. 5).
$$ \varvec{\varTheta}_{\text{u}} =\varvec{\varTheta}_{\text{i}} - \varvec{F}_{\text{x}} *\left( {\varvec{\varTheta}_{\text{i}} -\varvec{\varTheta}_{\text{e}} } \right) $$
(5)
The F X-value depends on form, location and function of the building component (see Fig. 4). The Fx-values for components adjacent to the ground depend on a characteristic value for the base slab (B’). B’ depends on the ratio of the floor slab area (A G) to the perimeter (P) of the slab (see Eq. 6).
$$ \varvec{B^{\prime}} = \frac{{\varvec{A}_{\text{G}} }}{{\left( {0.5*\varvec{P}} \right)}} $$
(6)
For the first version of the tool presented here, further assumptions are made for simplification:

  • No mechanical air conditioning (ventilation, cooling, humidification)

  • Single-zone model

  • No cooling demand

  • Global consideration of thermal heat bridges (ΔU WB = 0.05 W/m2 K)

2.3 Output

In order to provide the planner with insight into the calculation, partial results for sources and sinks are output for each module. This makes it possible to identify potential for improving the energy efficiency. Measures to increase energy efficiency can have a different impact depending on the type of building, the geometry and specific boundary conditions. Understanding the impact of individual measures, e.g. increasing the window area, on the total energy demand helps the planner to optimize the building design.

GH provides a range of possibilities for visualizing the results, such as a diagram or colour shading. These help the planner quickly comprehend the influence of changes to the building’s design. Instant visual feedback is especially important for designers used to working with visual information. While modelling, creating variants and adapting the design, the results are displayed simultaneously in a second viewport, as shown in Fig. 6. In addition, the results can be exported to spreadsheets for further processing in corresponding software.
Fig. 6

Screenshot of Rhino viewports for parallel modelling and result feedback

2.4 Verification

The application of the developed tool was verified using three reference buildings, all residential buildings of different types and sizes: a detached single family house (SFH), a multi-family house (MFH) and an apartment block (AB). A study by the Institut Wohnen und Umwelt (Loga et al. 2015) shows that those three categories correspond to the whole range of residential buildings in Germany. Furthermore, their geometric characteristics cover the range of typical building forms.

The tool’s calculation was additionally verified using two commercial software products based on DIN V 18599: “ZUB Helena® EnEV 2014” V 7.31 by ZUB Systems GmbH (S1) and “EnEV-Wärme&Dampf” V 15.38 by ROWA Soft GmbH (S2). Both software applications have been certified by a joint quality association “18599-Gütegemeinschaft e.V.” (Oschatz 2015).

The material and boundary conditions were input identically in all three tools (see Table 1). The u-values are based on the reference values of EnEV.
Table 1

Input parameters and boundary conditions

Building component

U-value [W/(m2 K)]

Exterior wall, floor to exterior

0.28

Roof, ceiling to unheated roof

0.20

Cellar wall to unheated cellar, floor to unheated cellar

0.35

Window

1.30

Door

1.80

Boundary conditions

 Construction type

Medium heavy

 C wirk

90.00 Wh/(m2 K)

 Thermal bridges (ΔU WB)

0.05 W/(m2 K)

 Minimum external air exchange (use)

0.5 h−1

 Infiltration und windows (Cat. I/Blower door test)

0.6 h−1

 Heating set point

20 °C

 Daily operational hours (t NA)

17 h

 Temperature setback (at night) (Δθi,NA)

4 K

 Internal loads q i

SFH: 45 Wh/(m2 d); MFH: 90 Wh/(m2 d)

 Climate

Potsdam, Germany

2.5 Modelling the reference buildings

The three reference buildings are modelled in Rhino. The thermal models are shown in Fig. 7. The grey surfaces are not considered as part of the thermal building envelope. The first reference building is a typical, detached single family house (SFH) with two storeys. The uppermost ceiling faces an unheated roof, while the floor adjoins an unheated cellar. The second building is a five-storey detached multi-family house (MFH). The cellar is unheated, but the staircase is heated and extends into the unheated cellar. The third building is a long, five-storey apartment block (AB) with an unheated cellar.
Fig. 7

Thermal model of reference buildings

The characteristic values for all three buildings are shown in Table 2.
Table 2

Characteristic values of reference buildings

 

SFH

MFH

AB

Storey height

2.70 m

3.10 m

2.77 m

Gross volume

368.29 m3

3528.62 m3

15974.36 m3

Net volume

279.91 m3

2822.90 m3

12,779.49 m3

Useable floor space AN

117.86 m2

997.12 m2

5111.8 m2

Envelope A

310.97 m2

1446.14 m2

6331.43 m2

A/V

0.84

0.41

0.40

Wall to exterior

2649.13 m2

727.33 m2

102.28 m2

Window (wall)

1003.28 m2

206.68 m2

26.81 m2

Roof

1339.51 m2

226.50 m2

58.07 m2

Ceiling to unheated roof

0.00 m2

0.00 m2

41.99 m2

Floor to exterior

0.00 m2

20.67 m2

0.00 m2

Cellar wall/floor to unheated cellar

79.56 m2

182.03 m2

1339.51 m2

F X (cellar wall/Floor to unheated cellar)

0.7

0.65

0.65

Floor to ground

 

23.08 m2

 

F X (floor to ground)

 

0.45

 

3 Comparison of results

The results of the calculated annual heating demand produced by the commercial softwares 1 and 2 (S1, S2) and the tool (T) are normed to those of S1. Table 3 shows that all three systems produced similar results, with the highest relative deviation occurring in the results for the SFH.
Table 3

Comparison of annual heating demand

 

SFH

MFH

AB

Heating demand

 S1

9110 kWh

53,844 kWh

235,186 kWh

 S2

9481 kWh

54,391 kWh

235,000 kWh

 T

9545 kWh

54,673 kWh

238,849 kWh

Deviation

 S1

100.0%

100.0%

100.0%

 S2

104.1%

101.0%

99.9%

 T

104.8%

101.5%

101.6%

To identify the reasons for the differences, all sinks and sources are compared individually for each reference building (see Fig. 8)

Fig. 8

Comparison of sinks and sources (%)

The high difference between S1 and S2 observed in the solar gains from windows (Qsol,trans) for the SFH can most likely be attributed to an input error, especially as this deviation does not appear for the other two building types. Nevertheless, it was impossible to find the error, despite carefully checking the input values. For the SFH and MFH, there are slight differences of solar gains through opaque components (Qsource,op). These gains only have a minimal effect on the total balance and are therefore negligible.

A significant deviation between the tool and S1 can be observed in QT and QV. The deviation is highest for the SFH. As a result, the deviation in Qh,b with 4.8% is also greatest for the SFH.

According to DIN V 18599-10, a factor for partly heated floor areas (atb) is defined, which equals 0.25 for single family houses and 0.15 for multi-family houses. atb is considered when calculating the indoor temperature for balancing single-zone models. This factor has not yet been integrated in the tool as in future multi-zone models seem to be the more effective model.

To analyse the effect of atb on the results, it was excluded from software 1. The results (S1B) are shown in Table 4. For the SFH, the new results are 5% higher (1.7% for MFH and 1.6% for AB) and are similar to the results of the tool. The maximum deviation between the tool and S1B is now only 0.2%.
Table 4

Comparison of annual heating demand excluding the factor for partly heated floor areas

 

SFH (%)

MFH (%)

AB (%)

Deviation

 S1

100.0

100.0

100.0

 S1 B

105.0

101.7

101.6

 T

104.8

101.5

101.6

3.1 Sensitivity of F X-values

As explained in Sect. 2.2, the F X-value for components that adjoin the ground depends on its geometric characteristics. To further simplify the input process, the sensitivity of the F X-value was analysed. The DIN V 18599-2 permits the use of a simplified, general F X-value of 0.7 for all building components adjoining the ground. The deviation of the calculated results when applying the simplified and detailed F X-values was examined. The differences for the three reference buildings are shown in Table 5. For the SFH, no deviation can be observed because the precise F X-value is also 0.7. The highest difference occurs for the MFH: the deviation of 1.33% is small enough to be negligible in the early design stage. As such, the use of the simplified F X-value is an acceptable approach.
Table 5

Deviation of results using simplified and exact F X-values

Reference building

Annual heating demand with detailed F X-values (kWh/a)

Annual heating demand with simplified F X-values (kWh/a)

Deviation (%)

SFH

9544

9544

0.00

MFH

54,673

55,402

1.33

AB

238,848

240,857

0.84

3.2 Example of application

To demonstrate the advantages of parametric input for the design of energy efficient buildings in the early design stages, variants of a MFH were generated and analysed using the tool. A five-storey building with a rectangular floor plan served as basis. A basement was not incorporated, and the U-values from Table 1 were used. The percentage of window area for the exterior walls was set to 20% for all façades.

In the first step, it was stipulated that the gross floor area (GFA) and the number of storeys should stay the same for all variants, but that the length and width of the building can be changed parametrically. The GFA is therefore the product of width (X), length (Y) and the amount of storeys (S): GFA = X × Y × S. The value of X is varied using a slider and the corresponding value for Y for the five-storey building calculated as: Y = GFA/(X × 5). The ratio of width to length of the building can be easily changed by adjusting the slider to vary the value for X. The heating demand is calculated in real time for each variant. This can be used to intuitively find a solution with minimum heating demand—i.e. the optimum ratio of X/Y. Three exemplary variants (A1−A3) are shown in Fig. 9. The minimum heating demand was found for variant A2 with a square floor plan, which can be explained by the smallest A/V ratio of this variant, resulting in the smallest transmission heat losses.
Fig. 9

Parametric variants of MFH

In the second step, the number of storeys was changed for the variant with a square floor plan. Three examples (B1–B3) with the same GFA are shown in Fig. 9. The minimum heating demand is achieved by variant B2 with three storeys. Although the ratio of A/V is bigger than in A2, the heating demand is slightly smaller.

4 Conclusion

The implementation of a quasi-steady state monthly balancing method in a parametric design software presented in this paper makes it possible to calculate a building’s energy demand in real time. A comparison of the developed tool’s results for three residential buildings with the results produced by an accredited commercial software product showed only small differences. As this new tool is conceived for the early design stages, this degree of differences is negligible.

The main advantage in comparison with commercial quasi-steady state tools is the parametric input of geometry, materials and boundary conditions. This allows the planner to easily change parameters and generate new variants. The primary advantage of the developed tool over other plug-ins for parametric design-based dynamic building performance simulation is the much improved computation time. While changing the design, the planner receives continuous feedback on the energy performance in real time. This provides the necessary basis for optimization processes, performed either manually by the planner or using computational optimizers, e.g. genetic algorithms.

The example application shows the great potential of being able to reduce energy demand by quickly and intuitively optimizing the base form. In this example, only one parameter was changed, but the approach can be applied analogously for the window layout or material selection. This demonstrates the great advantage of parametric control. In the future, additional case studies should be carried out to further validate the applicability.

The tool currently only calculates the heating demand of residential buildings. In future developments, further calculations, e.g. cooling demand, can easily be incorporated. The implementation of different thermal zones within a building is a further area of improvement that would enable the application of this method for non-residential buildings according to DIN V 18599. Currently, the tool is designed for application within Germany. In the future, international standards such as ISO 13790 (ISO 2008) or further national standards can be implemented to allow for a wider application.

Notes

Acknowledgements

This study was carried out as part of the research project ‘Integrated Life Cycle Optimization’ funded by the German Federal Ministry for the Environment, Nature Conservation, Building and Nuclear Safety through the research initiative ZukunftBau.

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Copyright information

© Joint Center on Global Change and Earth System Science of the University of Maryland and Beijing Normal University and Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Bauhaus-Universität WeimarWeimarGermany
  2. 2.Fraunhofer-Institut für Mikrostruktur von Werkstoffen und Systemen IMWSWeimarGermany

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