PERFORMANCE QUANTIFICATION OF BUILDING TECHNOLOGIES WITHIN THE SOUTH AFRICAN CLIMATIC REGIONS

Elsje C. BALZUN 1

Dirk C.U. CONRADIE 2

1 CSIR, Built Environment, Email: EBalzun@csir.co.za

2 CSIR, Built Environment, Email: DConradi@csir.co.za

Keywords: Climatic map, climatic regions, innovative building technologies, predictive building performance

Abstract

Developing countries’ advancement necessitates the construction of residential and social infrastructure at an ever-increasing rate. The progression should not only consider the rate of growth, but also the efficiency of the structures within its climatic context, to optimise occupant thermal comfort and alleviate energy consumption. Masonry building is predominantly used within South Africa, even though there are various certified innovative building technologies (IBT’s) available. A misconception exists within South Africa that IBT’s (certified building systems excluding masonry) have inferior overall performance to that of masonry building. The Decision Support Model for Innovative Building Technologies is a software tool which addresses this misconception, and assists planners and designers to select an appropriate building system for the climatic context and region of the proposed site. The newly developed CSIR Köppen-Geiger climatic map (14 climatic zones as opposed to the six-zone model of SANS 204) of South Africa was used in the Decision Support Model for Innovative Building Technologies to allow for a comprehensive understanding of the particular climate of the proposed site. A virtual model was created, and correlated to that of a measured notional building to ensure accuracy of the simulated data. Weather files were created for each of the identified Köppen-Geiger climatic regions as well as virtual models of each representative IBT and masonry building system. The various virtual models were simulated within each of the climatic regions to determine each system’s thermal performance. Constants (measured in kW/h) were derived from the simulations, and formed the quantitative segment of the software. The Agrément certification of each system was evaluated and graded qualitatively on a five point scale. This allows the user to evaluate the energy requirement, durability, acoustic performance, condensation and fire performance of the proposed building system. In conclusion, the software authoratively predicts the performance of each building system. It equips the user to select a warranted resolution/s for the proposed site, whether it be an IBT system or masonry construction, consequentially ensuring the design of comfortable and energy-efficient buildings within South Africa’s diverse climate.

1. Introduction

A building should enable the building occupants to conduct activities without the hindrance and/ or restrictions of the environment. This can be attained by adequate planning, functionality and durability of the building. Each component, including materials and services, should contribute to the over-all performance of the structure. It is therefore important to predict a building’s likely performance with regards aspects such as energy use, whilst providing a comfortable thermal environment for the building occupants. According to Van Straaten (1967) there are three primary aspects to consider when investigating the thermal performance in the design of a building within South Africa. Firstly, an assessment should be conducted to determine optimal interior conditions which are favourable for the health, safety, comfort and overall well-being of the occupant. Secondly, climatic conditions should be considered to enable an optimal design which best suits the distinct conditions of the region. Lastly, the physical properties of structural elements used and the design strategy should enable optimal control of the interior environment.

The Decision Support Model for Innovative Building Technologies software was developed to provide an answer to the complex question of which building technology (IBT) would be appropriate for specific locations in South Africa. Along with other performance metrics the characteristics of the specific climate is very important.

The objective of the research was to create a multi criteria decision support software that is easy to use by architects and designers to assess the suitability of various IBT’s in different locations. The research aim was to analyse the characteristics of 27 different IBT’s with regards thermal performance in specific climatic regions, building performance metrics (five factors) and construction supply chain management (five factors).

The first step of the research was to quantify the South African climate by the development of a detailed new Köppen-Geiger climatic map of South Africa that delineated the different climatic regions. The second step was to build thermal performance models of 27 IBT’s currently available in South Africa and by means of predictive building simulation to determine their performance in the 14 Köppen-Geiger climatic regions found in South Africa.

2. Climatic classification

2.1 South African National Standards – SANS 204 and 10400XA

The current climatic classification in South Africa (Figure 1) is in the South African National Standard 204: Energy Efficiency in Buildings (SANS 204). This classification recognizes only six climatic zones. Each of the climate zones have representative tables which provide the solar exposure factors as well as the coefficients regarding overhang/ height (P/H) factors for the eight orientations stipulated in SANS 204 (SANS 204:2011). This classification enables basic climatic responsive design and attempts to regulate the maximum allowable energy consumption, demand and orientation of various building typologies.

Figure 1 Climatic zones of South Africa as per SANS 204:2011

It became evident during an analysis of this map and the standard that it is inadequate to optimally support passive design and the performance of different building technologies within different climatic regions. Accurate building performance requires the use of predictive building computational performance (simulation software). In addition simulation software also requires a comprehensive array of climatic data, which the SANS 204 climatic zones do not provide (Conradie et al., 2012a).

2.2 Köppen-Geiger climate classification

The first quantitative climatic classification was completed by Waldimir Köppen in 1900. Rudolf Geiger elaborated the map in 1961. More recently, in 2005, Austrian researchers produced a contemporary version of the Köppen-Geiger map (Kottek et al., 2006). The Köppen-Geiger type of climatic map is after more than a century still the prevailing and most extensively used map. In modern climatic work it is often used to map predicted climate change using different scenarios. The climate of a region refers to the development of weather conditions which are prevalent to an area for a duration such as 30 years (Conradie, 2014).

2.2.1 Classification of the Köppen-Geiger climatic map

Köppen initially classified climatic regions in accordance with vegetation, as natural plant species are good indicators of climatic domains. The grouping was done according to the vegetation classification of the botanist, De Candolle (Kottek et al., 2006). The various zones were labelled alphabetically, those being: “A” - equatorial zone, “B” – arid zone, “C” – warm temperate zone, “D” – snow zone and “E” – polar zone

A second and third letter was added by Köppen to the zone classification of De Candolle, to allow for a refined categorisation. The second letter represents the precipitation of the specified region, and the third letter indicates the temperature.

Köppen combined the letters as described above, to define the various climatic regions of the world. The Council for Scientific and Industrial Research (CSIR) created a new Köppen-Geiger climatic map to accurately classify the climatic regions of South Africa, to support passive design and also the performance of different building technologies. The Köppen-Geiger climatic zones found in South Africa are outlined in Table 2 and illustrated in Figure 2.

Table 1 Köppen-Geiger categories (Conradie et al., 2012b)

Main climates Precipitation Temperature

Symbol Description Symbol Description Symbol Description

A Equatorial W Desert h Hot arid

B Arid S Steppe k Cold arid

C Warm temperate f Fully humid a Hot summer

D Snow s Summer dry b Warm summer

E Polar w Winter dry c Cool summer

m Monsoonal d Extremely continental

F Polar frost

T Polar tundra

Table 2 Köppen-Geiger climatic zones of South Africa

Colour code Type Description

Aw Equatorial, Winter dry

BWh Arid, Desert, Hot arid

BWk Arid, Desert, Cold arid

BSh Arid, Steppe, Hot arid

BSk Arid, Steppe, Cold arid

Csa Warm temperate, Summer dry, Hot summer

Csb Warm temperate, Summer dry, Warm summer

Cwa Warm temperate, Winter dry, Hot summer

Cwb Warm temperate, Winter dry, Warm summer

Cwc Warm temperate, Winter dry, Cool summer

Cfa Warm temperate, Fully humid, Hot summer

Cfb Warm temperate, Fully humid, Warm summer

Cfc Warm temperate, Fully humid Cool summer

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Figure 2 CSIR Köppen-Geiger map of South Africa (Conradie et al., 2012a), modified by author

To address the shortcomings of abovementioned climatic map it was decided to create a new high resolution Köppen-Geiger climatic map using the formulas as described by Rubel et al. (2010) (Figure 2). Comprehensive (1 km x 1 km resolution) historic climatic data of 20 year’s temperature and precipitation was obtained from the South African Agricultural Research Council. The climatic map was created by means of the ArcMap GIS software, using the formulas as defined by Kottek (2006) (Conradie, 2012). The map provided a method to group cities and towns within similar climatic regions. In addition detailed weather files, obtained from the Meteonorm software, were used to run the detailed thermal simulations of the different IBT’s. The CSIR Köppen-Geiger map was inter alia developed to assist building designers to define building design strategies during the early design stages. This facilitates decision-support to create climatic responsive structures that use less energy and is thermally comfortable to the end-user. The climatic classification map was also utilised by the CSIR to study the prospective use of bioclimatic design tools to address the question of climatic responsive design.

3. Classification of Innovative Building Technologies (IBT’s) systems

The next research step was to classify the different IBT’s into groups of similar characteristics to facilitate detailed performance analysis. Within the context of this paper an IBT refers to a building system which has been certified by Agrément South African certification board. Furthermore the term IBT excludes masonry, which is currently predominantly used within the South African building sector. A misconception exists within South Africa that IBT’s are inferior when compared to masonry construction and that heavy weight construction is preferable due to the thermal mass it provides. The use of appropriate building structures and methods are neglected in social infrastructure, such as schools. Schools do not generally make use of air conditioning and appropriate building system selection is therefore imperative. The appropriate selection could advance the thermal performance and palpable thermal comfort of the building.

3.1 IBT classification

The holistic performance of a building system is important when selecting a building system. Each building system’s performance that includes factors such as thermal, condensation and durability differs significantly within the various climatic regions. Each climatic region poses different challenges.

The IBT classification was developed to enable the quantification of various systems’ thermal and structural performance. The systems were categorised according to their technical composition. A quantative and qualitative investigation was conducted on each of the systems’ superstructure. Thermal simulation modelling was performed on the representative systems (Table 3 – highlighted in green) of each of the categories. Due to time and budget constraints the representative system’s findings were applied to the sub-group of each category. The categories were labelled alphabetically from “A” to “G”, ranging from light weight to heavy weight construction.

Table 3 Categorisation of IBT systems

Classification label Category Name of building system

A Light building system (LBS) with steel structural frame

Vela building system Amsa building system Alternative steel building system FSM building system Space frame building System

B Light building system (LBS) with structural steel frame and insulated foundations

Imison 3 building system Imison stud building system

C Light building system (LBS) with panels and light weight concrete

Goldflex 800 building system Goldflex 100 building system Goldflex 800 seismic building system

D Hybrid building system (HBS)

Automapolyblok building system Aruba building system Blast building system Insulated concrete panel building system Rapidwall building system Styrox building system

E Heavy weight building system (HWBS) with panels and dense concrete

Banbric building system Robust building system

F Heavy weight building system (HWBS) with building blocks

BESA 2 building system Hydroform building system Izoblock building system

G Masonry construction Masonry

The table above designates the categorisation and selection of building systems that were implemented within the Decision Support Model for IBT’s software.

4. Thermal performance simulations

Simulation software requires reliable and detailed climatic data to perform a quantified building performance analysis. There is still a lack of usable weather data (e.g. tmy. tmy2 and iwec) in South Africa, which is widely available in the USA. In the interim the Meteonorm software was used in this project to create weather files. This weather data is essential to inform and support climatic responsive design. The creation

of the CSIR Köppen-Geiger map was the first step to fill the void in South Africa regarding the necessity of detailed climatic data.

A calibrated model of a typical classroom was developed using EcotectTM

. To ensure accuracy, the results from the simulation model were compared with in-situ measured data. A detailed model was created for each of the representative IBT systems. A thermal simulation was conducted on each of the systems in 34 cities and towns in South Africa, for each of the climatic regions and grouped according to the Köppen-Geiger climatic classification that was created. The roof and the other elements of the virtual building were the same for all the systems. In each case the walls were changed to simulate the desired building system. A total of 238 simulations were undertaken. The annual cooling and heating (kWh) energy required were calculated, to determine the amount of energy needed to make the building interior comfortable within each of the climatic region. To facilitate processing the simulation results were grouped into corresponding climatic regions and normalized to a score out of five. Five denotes the best performance in the group of IBT systems and one the worst.

5. Decision Support Model for IBT’s

There are a large number of building systems and building products available on the market today. It is exceptionally difficult for architects of e.g. schools and hospitals (social infrastructure) to determine an appropriate building system for the specific climatic region and location. Furthermore it is unpractical to investigate each system’s properties every time to make an informed decision regarding the use of an appropriate system for the specific climatic region. There is therefore a need for a bespoke tool to assist architects and designers as to which building technology will be appropriate for the site and specific climate.

The Decision Support Model for IBT’s is constituted of an essential set of related technologies and methods. The software was developed using Visual Basic for Applications (VBA). The development of the software was undertaken in five phases.

Phase 1: The first phase was the creation of a new Köppen-Geiger climatic map, to quantify the climatic conditions of South Africa. The climatic map provided a climatic grouping background as a basis for the subsequent detailed building system performance simulations.

Phase 2: This phase included the classification of set of 27 IBT systems according to their thermal performance and composition. The systems were arranged from light weight to heavy weight building systems. Figure 3 below illustrates the vast difference in U-values between the various building systems that range from light weight (Classification A) to heavy weight (Classification F) (Table 3).

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Figure 3 U-value comparison between various building systems

Phase 3: Detailed simulation models were created for each for the representative IBT systems (Table 3). The results of these simulations were programmed into the software as a normalized constant value. The software considers the selected climatic region and calculates the performance of the various systems according to the selected climatic region and other criteria..

Phase 4: A qualitative section was created by analysing various Agrément certificates for each of the building systems. This was achieved by rating each aspect of the systems on a scale of five. The aspects which were rated are acoustic performance, energy requirement, condensation, fire performance and durability. The predetermined values form part of the software. Figure 4 below

illustrates the default relative importance or weighting that was applied to the five performance ranking factors.

Figure 4 Performance ranking according to Agrément certificates, from the Decision Support Model for IBT’s

Phase 5: The construction chain management was studied of each of the 27 systems. Suitability was determined for construction sites that vary from urban to inaccessible deep rural. The metrics included economies of scale, distance from suppliers, local labour force, lead time flexibility, distance from suppliers.

Figure 5 Construction supply chain management, from the Decision Support Model for IBT’s

The Decision Support Model for IBT’s therefore considers 11 factors before calculating which system would be appropriate for the user’s need. The process is as follows:

1. Factor 1: The user selects the relevant climatic region using a convenient CSIR Köppen-Geiger overlay on Google Earth.

2. Factor 2 – 6: The performance ranking is automatically calculated. Each of the five performance rankings has different weights. For example acoustic performance has a weight of 0.13 and durability 0.235.

3. Factor 7 – 11: The user can select the appropriate “Construction supply chain” management characteristic which is applicable to the specific site.

Once all the criteria have been selected, the score for each building system is calculated and immediately displayed. A colour-code is applied to the systems according to the total performance score of each system. The applied colour-coding, illustrated in Figure 6, assists the user to easily identify which building systems would be preferable according to the set of selected criteria.

Figure 6 Total performance ranking, of the Decision Support Model for IBT’s (modified by author)

The software is unique as it incorporates the results of a comprehensive climatic analysis, extensive simulation modelling, analysis of the relevant Agrément certificates and supply chain management decision-making in a single system as illustrated below.

Figure 7 Decision Support Model for IBT’s user interface

The software provides a convenient to use approach to examine multi-criteria regarding various climatic regions, multiple building systems and construction supply chain management. This has already assisted South African government departments to improve social infrastructure, without the necessity to undertake ab initio investigations of all the complex and interrelated factors before each project. The Decision Support Model for IBT’s therefore facilitates a multi-criteria decision-taking process within a complex environment.

6. Conclusion and Further Research

The Köppen-Geiger map provided a good method to group locations with similar climatic characteristics together. The detailed simulations were supported with weather files that were generated from the Meteonorm software. Due to the fact that the Köppen-Geiger climatic map is based on empirical functions of temperature and precipitation it cannot accurately predict the thermal comfort of the building occupant within a distinct climate. Thermal comfort is primarily determined by relative humidity and dry bulb temperature. To address this, CSIR is currently undertaking further research to create climatic maps that address the

shortcomings mentioned. Two maps have recently been created, i.e. a Standard Effective Temperature (SET) and a Degree Day map.

The SET map incorporated dry bulb temperature and relative humidity, however it did not correlate well with the annual cooling and heating demand. Supplementary to the SET climatic map, a Heating Degree Day (HDD) and Cooling Degree Day (CDD) climatic map (based on hourly calculations) was developed which correlates well with the cooling and heating energy demand of a proposed structure. Passive design development is further assisted by the inclusion of summer and winter angles as well as humidity lines. The Decision Support Model for IBT’s can be improved by incorporating the newly created maps into the software, for greater accuracy of the thermal performance of the building systems and the thermal comfort the building provides. The research team is currently improving the software with the inclusion of more criteria that will lead to improved prediction accuracy.

7. References

Agrometeorology Staff. 2001. ARC-ISCW Climate Information System. ARC-Institute for Soil, Climate and Water, Pretoria.

Conradie, D.C.U. 2014. The performance of Innovative Building Technologies in South Africa’s climatic zones. In Green Building Handbook, The Essential Guide, Vol 6.

Conradie, D.C.U. & Kumirai, T. 2012a. The creation of a South African Climate map for the quantification of appropriate passive design responses. In Proceedings of the 4

th CIB International Conference on Smart and

Sustainable Buildings, June 2012, Sao Paulo, pp. 195-203.

Conradie, D.C.U. & Kumirai, T. 2012b. Predictive performance simulations for a sustainable lecture building complex. In Proceedings of the 4

th CIB International Conference on Smart and Sustainable Buildings, June

2012, Sao Paulo.

Kottek, M., Grieser, J., Beck, C., Rudolf, B., & Rubel, F. 2006. World Map of the Köppen-Geiger climate classification updated. In: Meteorologische Zeitschrift, Vol. 15, No. 3, pp. 259-263.

SANS 204. 2011. South African National Standard. Energy efficiency in building, part 2: The application of the energy efficiency requirements for buildings with natural environmental control. SABS Standards Division.

Van Straaten, JF. 1967. Thermal Performance of Buildings. Amsterdam, London, New York: Elsevier Publishing Company.