Designing for South African Climate and Weather
To design energy efficient buildings using the correct combination of passive design strategies such as insulation, thermal mass and natural ventilation it is necessary to understand the particular climate very well.
Cape Town (I-Newswire) February 5, 2013 - 1. Introduction
To design energy efficient buildings using the correct combination of passive design strategies such as insulation, thermal mass and natural ventilation it is necessary to understand the particular climate very well. To perform a quantified building performance analysis by means of simulation software a detailed set of quantified climatic data is required.
The climate of an area is the averaging effect of weather conditions that have prevailed there over a long period of time such as 30 years. Due to the fact that earlier researchers did not have computers and electronic databases to research the gradual changes in climate Wladimir Köppen and Rudolf Geiger inter alia regarded climate as constant and used all of the sparse climate information available to compile a single climatic map (Rubel et al., 2010). Today we know that the climate is constantly changing over time due to a complex interaction of factors.
Climatic classification is an attempt to formalize the process of recognizing climatic similarity (Kruger, 2004). Some of the benefits of climatic classification are:
To indentify areas of influence of various climatic factors
To stimulate research to identify the controlling processes of climate
To inform an appropriate scientific response to building design
2 Climatic classification
2.1 The climate regions of South Africa
Low and Rebelo subdivided the seven vegetation biomes in South Africa into 68 vegetation types which consist of different concentrations of vegetation species. These types are mainly determined by climate, but sheltering, soil type, occurrence of veld fires, browsers such as goats and wild life, elevation and inclination and other minor factors also play a role. The boundaries for climatic regions were determined by making use of these vegetation types. Combinations of smaller vegetation type areas into larger regions that are easier to map and described from a climatic point of view, were made (Kruger, 2004).
Figure 1 illustrates these climatic regions. Nine Savanna-type climatic regions have been identified, six Grassland-type regions, five Karoo-type regions, two Fynbos-type regions, one Forest-type region and one Desert-type region.
2.2 SANS 204-2
The SANS 204-2 (2008) standard recognizes six main climatic regions in South Africa (Figure 2). It is an attempt to introduce a quantified view of climate into the National Building Standards. If this classification is compared with more detailed research work it is clear that much refinement would be required to support designers within the built environment. The chapter about fenestration in said document provides a detailed description of the calculation of conductance and solar heat gain for glazing elements supported by extensive look-up tables and diagrams.
For each of the six climatic zones tables are provided that gives the solar exposure factors and coefficients (SHGC) for various overhang/ height (P/H) factors for eight main orientation sectors. The standard is useful for initial desktop calculations. It is clear from SANS 204-2 (2008) what the beneficial impact of fenestration design in combination with appropriate sun protection could be. This should be quantified with more detailed calculations preferably with simulation software once the designer has determined the sun protection devices that will be used.
Green building, greed design, climate change
Figure 2: Climatic zone map (SANS 204-2, 2008)
Table 2 below is a composite table that combines the description of SANS 204 climatic regions with the latest CSIR Köppen map and also some illustrative temperature characteristics of some cities that were obtained from Meteonorm. It is interesting to note that the suggested Köppen classifications of the Meteonorm is a comprehensive climatological database for solar energy and climatological calculations applications. The Southern African data is base on 45 weather stations, that is enough to base fairly accurate building performance calculations on.
Meteonorm files differ slightly from the CSIR classification. The reason is that the CSIR Köppen map is based on a very high resolution 1 km x 1 km grid using 20 years worth of monthly temperature and precipitation data ranging from 1985 to 2005. Although the Köppen map is only based on precipitation and temperature it is a convenient way to check the validity of weather files used in more detailed building
performance simulations. The reason for this is that if a particular weather file’s Köppen classification differs then it very likely used different data for precipitation and temperature.
The first three columns of Table 2 illustrate the SANS 204-2 climatic classification. The colours are as used in the SANS-204 norm. Columns four to six contains additional information. Column four contains the CSIR Köppen- Geiger classification that was derived from 20 years of monthly precipitation and temperature data on a 1 km x 1 km grid. The colours used here is identical to those used in the CSIR Köppen map. The formulae as described in detail by Kottek (2006) were used to calculate the classifications. It is clear that the SANS-204 classification is very coarse and an over simplification of the real situation. In the right hand columns some temperature graphs are shown that give an indication of the monthly and annual maximum, minimum and average temperature profiles.
2 Weather files
To calculate or simulate the expected building performance by means of desktop, spreadsheet or advanced software such as EnergyPlus or Ecotect reliable weather files are required. Weather files are normally created for different purposes as discussed below. Weather files typically contain seven main climatic aspects with an hourly interval. These aspects are dry bulb temperature, humidity, direct solar radiation, indirect solar radiation, wind strength/ direction, amount of cloud and precipitation.
2.1 Typical Meteorological Year (TMY)
A TMY is a set of hourly values of solar radiation and meteorological elements for a one year period. It consists of typical months selected from actual observed weather files from different years to form a complete year. The TMY weather files are intended for simulations of energy conversion and building systems. Due to the selection criteria, TMYs are not appropriate for simulations of wind energy conversion systems. (NREL, 1995)
A TMY weather file provides a standard for hourly data for solar radiation and other meteorological elements that permit performance comparisons of system types and configurations for one or more locations. A TMY is not necessarily a good indicator of conditions over the next year, or even the next 5 years. It represents typical conditions over a long period of time, such as 30 years. This fact means that they are not suitable for designing systems and their components to meet worst-case conditions occurring at a location. (NREL, 1995)
Both the original TMY and improved TMY2 data sets were created using similar procedures that were developed by Sandia National Laboratories. The Sandia method is an empirical approach that selects individual months from different years from the period of record. For example, in the case of the
NSRDB that contains 30 years of data, all 30 Months of January are examined and the one judged most typical is selected to be included in the TMY. The other months of the year are treated likewise. The 12 selected typical months are then concatenated to form a complete typical year. (NREL, 1995)
The 12 selected typical months for each station were chosen from statistics determined by using the five elements global horizontal radiation, direct normal radiation, dry bulb temperature, dew point temperature, and wind speed. These elements are considered the most important for simulation of solar energy conversion systems and building systems. For other elements in the TMY2 format the selected months may or may not be typical. Cloud cover, which correlates well with solar radiation, is probably reasonably typical. Other elements, such as snow depth, are not related to the elements used for selection; consequently, their values may not be typical. Even though wind speed was used in the selection of the typical months, its relatively low weighting with respect to the other weighted elements prevents it from being sufficiently typical for simulation of wind energy conversion systems and hence architectural designs where wind direction and speed is critical. (NREL, 1995)
2.2 International Weather for Energy Calculations (IWEC)
The IWEC weather files are the result of ASHRAE Research Project 1015 by Numerical Logics and Bodycote Materials Testing Canada for ASHRAE Technical Committee 4.2 Weather Information. The IWEC weather files are described as “typical” weather files suitable for use with building energy simulation programs. IWEC weather data for more than 2100 locations are now available in EnergyPlus weather format This include 1042 locations in the USA, 71 locations in Canada, and more than 1000 locations in 100 other countries throughout the world. The weather data are arranged by World Meteorological Organization region and Country.
The files are derived from up to 18 years of DATSAV3 hourly weather data originally archived at the U.S. National Climatic Data Center. The weather data is supplemented by solar radiation estimated on an hourly basis from earth-sun geometry and hourly weather elements, particularly cloud amount information. An IWEC CD-ROM is available from ASHRAE. (ASHRAE, 2001)
The Department of Energy has licensed the IWEC data from ASHRAE. The license with ASHRAE allows the U.S.A. Department of Energy to:
Distribute versions of the individual IWEC files in converted format suitable for EnergyPlus (EPW).
Make the EnergyPlus versions of the IWEC files available to users at no cost via the EnergyPlus web site.
There are unfortunately only two IWEC weather files available for South Africa, i.e. Cape Town 688160 and Johannesburg 683680 obtainable from the EnergyPlus website.
2.3 Köppen-Geiger classification
While there are many different approaches to climatic classification empirical classifications such as the Köppen-Geiger classification is the most widely used. The first quantitative classification of world climates was presented by the German scientist Wladimir Köppen (1846 – 1940) in 1900. It has been available as a world map updated in 1954 and 1961 by Rudolf Geiger. Köppen was a trained plant physiologist and realised that plants are indicators for many climatic elements. His effective classification was constructed on the basis of five main vegetation groups determined by the French botanist De Candolle that referred to the climate zones of the ancient Greeks (Kottek, 2006). The five vegetation groups of Köppen distinguish between plants of the equatorial zone (A), the arid zone (B), the warm temperate zone (C), the snow zone (D) and the polar zone (E). A second letter in the classification considers the precipitation and a third letter the air temperature.
The CSIR created a new Köppen-Geiger map to quantify the current climatic conditions accurately as illustrated in Figure 3. This classification uses a concatenation of a maximum of three alphabetic characters that describe the main climatic category, amount of precipitation and temperature characteristics.
3 Climate change
All indications are that we can expect a significant amount of climate change in South Africa. This will have a profound impact on the built environment and how buildings should be designed in the future.
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Published in:Science and Research
Published On:February 5, 2013
Print Release:Print Release
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