This section aims to provide a better understanding of the concepts and some of the methodological tools employed during the research process, particularly those related with weather simulation, which is the stepping stone for building energy simulation. It is also meant to provide an introductory understanding of bioclimatic design in architecture, which will be further explained in forthcoming chapters.
Although the close relationship between the build and local weather (environment) has been acknowledged in vernacular architecture since hundreds of years ago, it was not until the mid XXth century that the concept of ‘Modern Bioclimatic Architecture’ became part of the
architectural thought. The establishment of the International Society of Biometeorology by UNESCO in 1956 was a breakthrough in the understanding and widespread of the
relationship between weather and human wellbeing. Also, in 1953 the Olgay brothers published their classic book Design with Climate, Bioclimatic Approach to Architectural Regionalism, which is considered as the culmination of the introduction of the bioclimatic design concept to the modern architectural profession (Sayigh, et al., 1993, p. 522). In their book, the Olgay brothers introduced the Bioclimatic Chart, which manages to plot (define) ‘thermal comfort’ parameters specific for a given climatic location.
The bioclimatic chart manages to correlate several climatic parameters such as: wind speed, relative humidity, dry bulb temperature; the effect of air movement on vapour pressure; the effect of added moisture on high temperatures; and the correlation of radiation and dry bulb temperature. Further developments led to the ‘Building Bioclimatic Chart’ introduced by Givoni in 1969 which, in turn, manages to differentiate between external conditions and internal building conditions; it also correlates relevant passive heating/cooling strategies appropriate for any given studied location. Ever since, the building bioclimatic chart has been a very useful tool during the first stages of the architectural design process to select the most pertinent bioclimatic design strategies.
Nowadays, several software, such as Climate Consultant4, manages to plot the building bioclimatic chart for a selected location provided the input of the relevant weather file is available. By the simplification of such a process, architects can focus on devising the best way to pull together bioclimatic design strategies instead of spending a large amount of time plotting and charting. Therefore, it can be thought of as a pre-design analysis tool meant to be used at the sketch design stage. Figure 2.4 shows the building bioclimatic chart for San
4 Climate Consultant is a software developed by Robin Liggett and Murray Mine for the UCLA energy Design Tools Group. The building generates Building Bioclimatic Charts using climatic files on ‘epw.’ format. It also shows in a user friendly and graphic way the most relevant climate parameters for architectural design correlating them with comfort models such as the California energy Code Comfort Model and ASHRAE.
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Luis Potosi, which was generated with Climate Consultant software and an ‘epw’ file which uses weather data up to year 2010.
Figure 2.4 Building Bioclimatic Chart for San Luis Potosi City, generated by the author with Climate Consultant 5.5 software
The building bioclimatic chart plots the comfort zone and shows the different passive design strategies to achieve it as well as their effectiveness in doing so. This is the first step in the general architectural design process. It is not meant to be used as a ‘cake recipe’; instead, it is a tool that can help improve building energy performance and climate responsiveness in an energy efficient way through different alternatives/mixes of passive design strategies.
In the chart, the most effective passive design strategies for San Luis Potosi are: sun shading of windows, high thermal mass, internal heat gains, passive solar direct gain high mass. It also shows that heating might be needed; and that a building will provide thermal comfort 25% of the time without the implementation of any passive design strategy.
However, this analysis level can only be considered as general design guidelines and more complex energy simulations of buildings are necessary to assess their effectiveness. Finally, it is important to bear in mind that some of the strategies shown in the building bioclimatic chart might not be compatible between themselves – instead, they represent a range of ‘design opportunities’.
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2.7.1 Weather files and energy simulation
As an introduction, it is important to make a distinction between the most commonly used concepts in relation to weather and climate. Firstly, weather comprises the day-to-day changes in meteorological conditions, including temperature, rainfall, snow etc. Secondly, climate relates to average weather conditions based on, typically, 30 years of observations for any given location. Thirdly, microclimate is the term used to designate the climatic conditions directly surrounding the living organism (Sayigh, et al., 1993, p. 530). Finally, there is usually a big difference between the general climate of a region and the specific microclimates within the very same region (due to geographical factors etc.).
During the 1980s decade, the Model Year Analysis (MYA) was introduced and consisted of a modelling technique to obtain the best model that reflects the amount of solar radiation and the sunshine duration for specified locations. The technique is applied because climate in any specific location varies from one year to the next and, actually, will not repeat itself in the same location until 30 years (12 years in some cases).
Based on the cyclic nature of weather and having at least 10 years of observations on weather data, it is possible to produce a Climate Model Year (CMY). This model year is not an average year but a more sophisticated representation of climate in a specific location. One of the main advantages of this CMY is that it eventually allowed the simplification of climate computer modelling as well as the effects of climate on buildings. The CMY includes dry bulb temperature, wet bulb temperature, global (or direct) solar radiation, wind speed, wind direction and atmospheric pressure.
If sufficient locations in a region (or the world) are mapped using the model year technique, then it is possible to produce Model Year Climate Mapping (MYCM); this is relevant since climate has a very interactive nature with its surroundings. Therefore, it is possible to obtain the CMY for a location for which we do not have enough weather data (Sayigh, et al., 1993, p. 530). Such is the principle underlying weather generation software such as Meteonorm (which also uses more complicated algorithms included those from the IPCC).
In this research the weather file type used for simulations is the EnergyPlus weather format (epw) which is a standard format for weather simulation and is widely used by energy simulation software in the build environment and it was generated with Meteonorm as no robust weather files were available for San Luis Potosí City5. The epw format draws from
around 20 different sources from all over the world and includes the TMY and its further
5 Meteonorm employs datasets from years 1991-2010 for radiation and from years 2000-2009 for temperature to generate user defined weather files such as was the case for San Luis Potosi City, Mexico.
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developments (TMY2 and TMY3). Therefore, it is one of the most reliable standards for weather simulation and is the one used on this research.