Solar Access in Tropical Cities: Towards a Multicriteria Solar Envelope

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1 PLEA The 22 nd Conference on Passive and Low Energy Architecture. Beirut, Lebanon, November /5 Solar Access in Tropical Cities: Towards a Multicriteria Solar Envelope Rafael Silva Brandão 1 and Márcia Peinado Alucci 1 1 Laboratório de Conforto Ambiental e Eficiência Energética (LABAUT), Faculdade de Arquitetura e Urbanismo da Universidade de São Paulo, São Paulo, Brazil rafael.brandao@superig.com.br ABSTRACT: Solar access is an issue of growing importance, as buildings with environmental concerns rely more and more on solar systems for better performance and lower energy consumption. Most of the discussion, however, is being carried out in developed countries with temperate climate, where sunlight is desirable most of the year. In Brazilian cities, where climate is mostly tropical, solar radiation availability is highly increased and overheating becomes a major problem. This paper describes the development of a tool that would evaluate solar access, using as a study case the city of Sao Paulo, Brazil (23 37 S, W). The method, however, could be applied to any other location to which proper data is available. A procedure was formulated - using an anisotropic sky model, climatic data and geometry information - in order to calculate daylight availability and solar heat gain reduction. It predicts the impact of a building on energy consumption of neighbour sites for lighting, heating and cooling, and allows planners to verify impact of growing densities (and consequent higher obstruction angles) in urban energy performance. Conference Topic: 02 Sustainable Urban Design and Planning Keywords: solar access, urban planning, energy efficiency INTRODUCTION Solar access is an issue of growing importance, as buildings with environmental concerns rely more and more on solar systems for better performance and lower energy consumption. Most studies concerning this issue have been carried out in European or North American research centers, meaning they reflect a reality very different from that of tropical, developing countries cities. In Brazil, for instance, most cities are located in the tropical zone, which means higher solar angles, intense solar radiation and high temperatures. Even though these conditions make them more adequate for implementation of most solar technologies, local buildings may suffer from overheating. Brazilian cities also have a very fragmented urban structure, where high-rise side with single-family housing, zoning is not absolute and urban planning has to deal with heterogeneity. This paper describes the development of a tool that would evaluate solar access benefits, using as a study case the city of Sao Paulo, Brazil (23 37 S, W). The method, however, could be applied to any other location to which proper data is available. A procedure was formulated - using an anisotropic sky model, climatic data and geometry information - in order to calculate daylight availability and solar heat gain reduction. It predicts the impact of a building on energy consumption of neighbour sites for lighting and cooling, and allows planners to verify impact of new taller buildings in urban energy performance. In order to do that, the study object was divided in three interdependent parts: the source (sun and daylight), the interface (the city) and the final use of solar energy (building s passive and pro-active solar systems) (fig. 1). Environment City Building Systems Users Figure 1: Conceptual model of solar access variables: source, interface and final use Each part was studied separately, using existing models described in the 2 chapter of this paper. The final use of solar energy has both thermal and luminous aspects, which were put together by analysing energy consumption for heating, cooling

2 PLEA The 22 nd Conference on Passive and Low Energy Architecture. Beirut, Lebanon, November /5 and artificial lighting. By doing that it was possible to confront conflicting recommendations, in a way that they could be weighted against each other. 2. MODELLING 2.1 Sky modelling In order to model the source, radiation data was necessary. Some calculating methods were experimented, but the final hourly radiation used was provided by the campus station of the Laboratório de Micrometeorologia, Instituto de Astronomia, Geofísica e Ciências Atmosféricas da Universidade de Sao Paulo (IAG-USP). The sky was then divided in 145 areas, that, according to Kendrick [1], allows each area to be treated as a homogeneous point source. Radiance and luminance distribution were calculated, according to Brunger & Hoper [2] and Igawa & Nakamura [3] Sky temperature and emissivity was also calculated, in order to assess long-wave radiation loss to the sky, using Givoni s model [4]. Air temperature, relative humidity and cloud cover data from the Meteorological Station in Água Funda, Sao Paulo (WMO n ), was provided by the Laboratório de Meteorologia Aplicada a Sistemas de Tempo Regionais (MASTER/ IAG-USP). 2.2 Obstruction modelling Obstruction in this work always relates to a reference point, and it is defined by horizontal obstruction angles measured from the north axis and by vertical ones from the horizon (fig. 2). Plan 30m Section Building A 30m Building A θ v θ hf θ hi 1 PERSPECTIVA Figure 2: Indication of obstruction angles and positioning of the building in the Cartesian plane The building position and site plan dimensions are given by their coordinates in a relative Cartesian plan. Origin point is always the southwest corner of the site. The obstruction from new buildings is calculated here for the midpoint of each edge of the site [5]. 2.3 Daylight The daylight analysis was simplified. Only average illuminance levels were calculated based window/ floor ratio, using on the Frühling equation [6]. From Daylight [7] software simulations, it was verified that, for average values, this approach would give accurate enough results for urban scale studies [5]. However, it should be noted that responsibility for the distribution of daylight inside rooms was left 4 3 entirely to the architect. The calculations have only taken into account single window rooms. 2.4 Thermal Performance Thermal performance was evaluated by calculating internal temperature. This is made by first determining an instantaneous internal-external temperature difference ( T inst ), using a simple energy balance equation, adapted from Frota & Schiffer [8]: T inst = (Q SO + Q ST + Q LW + Q I )/q C + q V [eq. 1] Where: T inst is the temperature difference from outside; Q SO is the solar gain through opaque walls; Q SG is the solar gain through glazing; Q LW is the long-wave radiation exchange with the sky; Q I are the internal gains; q C is the temperature difference heat exchange coefficient; q V is the ventilation heat exchange coefficient. This temperature difference is added to the hourly external temperature, giving the instantaneous internal temperature T inst = T e + T inst [eq. 2] An average of the last 24 instantaneous values is then calculated (T im ), in order to determine the behaviour of very heavy construction. The supposed internal temperature (T SI ), which is the temperature the room would be at if there was no air conditioning system, is the average between this mean value and the instantaneous temperature. In order to account for mass effect, it was inserted a mass factor (m), that ranges from 0,2 to lightweight buildings to 1,0 for very heavy construction. Thus, T IS is determined by the following equation: T SI = m T im + (m-1) T inst [eq. 3] 2.5 Energy consumption calculations Artificial lighting energy is calculated hourly. It was assumed an automated on/off system turns on the lights in a room every time the mean value drops bellow a certain level. The energy consumption calculation method was adapted from Alucci [9] to an hourly basis. Air conditioning and heating are calculated using the degree hour (DH) methodology [9]. The base temperature for calculations is the neutral temperature [10], with a 2 C tolerance for cooling and a 4 C for heating. The cooling (L C ) and heating (L H ) loads are given by: L (C or H) = DH (C or H) q [eq. 4] where q is the sum of q C and q V. The air conditioning energy consumption in Wh is calculated by dividing the cooling loads (multiplied by 3,6 for unit coherence) by the air conditioning efficiency. The heating energy consumption in Wh is the heating load for that hour. It is important to note that even though most Brazilian dwellings don t have air conditioning or heating, this imaginary energy consumption indicates existence of discomfort. Though this method may provide fairly good indication of environmental

3 PLEA The 22 nd Conference on Passive and Low Energy Architecture. Beirut, Lebanon, November /5 quality, it does not take into account some important factor in tropical cities, like ventilation. However, it is fit to balance out obstruction effects on heating, cooling and artificial lighting demands of nearby spaces. 3. THE OBSTRUÇÃO 1.0 SOFTWARE The energy consumption results are shown either in absolute numbers or as the increase/decrease percentage comparing to the unobstructed situation. The graphs present the performance of the group of cells as a weighted average that takes into account the edge length and of each cell individually. In fig. 5 there is a chart of the data flux inside the spreadsheet. In order to make the previous calculations automatic, the Obstrução 1.0 Spreadsheet was developed in a Microsoft Excel format. It calculates the impact of a new building on its neighbours energy consumption, given a climate and site and building dimensions. To do so, it calculates the energy consumption for heating, cooling and artificial lighting for test cells, located on the midpoint of each edge of the site. The test cells are one-façade rooms, placed by the border of the site, facing the new obstructive building (fig. 3). Sheet 3 Sheet 2 Sheet 1 Obstructed zones Visualization Data input calculations INPUT Site dimensions Building dimensions Obstructede sky zones Site dimensions Building dimensions OUTPUT Air conditioning energy variation* Heating energy variation Lighting energy variation* Final energy consumption variation Visualization Obstructede sky zones Adjacent façades azimuth PLAN PERSPECTIVE Sheet 4 Radiation calculations Obstructed sky zone Adjacent façade azimuth Sky radiance distribution irradiance on adjacent façades horizontal irradiance Unobstructed and obstructed irradiance on tilted surfaces irradiance on adjacent façades MAD Mean point of the AD edge Figure 3: Test cell localization in the software Sheet 5 Temperature and airconditioning horizontal irradiance Air temperature Sky emissivity Test cell characteristics Dimension Thermal properties Occupation Air conditioning efficiency Air conditioning energy variation Heating energy variation* The software was designed using 7 sheets. Most users will only have access to the first one, called Data Input (fig. 4), which also provides final results. Sheet 6 Daylighti calculations Obstructed sky zones Adjacent façades azimuth Sky luminance distribution illuminance on adjacent façades SITE DATA (X,Y) BUILDING DATA (X,Y) BUILDING HEIGHT ENERGY CONSUMPTION GRAPHS Sheet 7 Lighting Iluminância nas fachadas adjacentes com e sem obstrução Test cell characteristics Dimensions Luminous properties Artificial lighting system Lighting energy variation** *Note that energy consumption here always refers to the neighbor buildings Figure 5: Information flux in the Obstruction 1.0 software The building input data determines obstruction for each cell. A sky zone is considered obstructed if its central point azimuth is between the initial and the final horizontal obstruction angle and if the altitude angle is lower than vertical obstruction angle. The second sheet provides visualization of the obstruction in an approximately cylindrical projection (fig. 6). ABSOLUTE ENERGY PERCENTUAL ENERGY FAÇADE ENERGY Figure 4: Sheet 1 in the Obstruction 1.0 software: data input The basic inputs are site and building coordinates and building height. Figure 6: Obstruction visualization in the software

4 PLEA The 22 nd Conference on Passive and Low Energy Architecture. Beirut, Lebanon, November /5 The cylindrical projection is rotated 90 clockwise for better visualization. The dark zones are obstructed and the top of the rectangle is the northern zone (azimuth 0 ). A sky zone is considered to be obstructed if its central point azimuth is between the initial and the final horizontal obstruction angle AND if the altitude angle is lower than the vertical obstruction angle. Each rectangle represents the obstruction of one of the test cells. The sky radiance and luminance modelling required some considerable processing capacity, since it multiplies 8760 radiation inputs by 145 zones, generating a table of over a million cells. Therefore, they were calculated in a different spreadsheet and only the final result was imported. That means that, in order to insert other climatic data, it would be necessary to repeat the process and import them again. The cell parameters dimensions, U-values for glazing and opaque, WWR, internal loads, mass factor, etc are defined in Sheet 5 - Temperature and air conditioning. Changing these values may alter significantly the results. Therefore, a precise hypothesis of the surrounding buildings is necessary for satisfactory calculations. Should the software be implemented in any city or town, this part of the spreadsheet should be accessed only by specialists from public administration and not by regular users. Total energy consumption increase or reduction is given separately for each final use (heating, cooling, lighting) in absolute numbers in the graphs in the top right sector. Below the data input there are tables that summarize the relative variations in energy consumption. At the bottom of the screen, graphs show the variation for each cell, which allows the study of different obstruction orientation. Thresholds for energy consumption increase have not been suggested, since it is a political matter. Planner should have in mind that the more restrictive the thresholds are, the lowest densities will be. 4. EXPLORING THE MODEL 4.1 A theoretical example In order to explore the potential of the spreadsheet, a pilot study was made using the building shown in fig. 7 B (0,84) N 2 (12,72) C (84,84) 3 (72,72) 1 (12,12) A (0,0) Figure 7: Example building 4 (72,12) D (84,0) The building is a 60m wide square oriented along the north-south axis and it is located 12 m far from the edge in order to simulate an infinite horizontal obstruction. First simulated height was 12 m. For the first simulations, test cells dimensions were set to 6x6x3 m. In later simulations they were reduced to a cubic proportion of 3x3x3 m. The other parameters for the simulations are given in the tab. I Table I: Parameters for the pilot study Parameter Value Parameter Value WWR 0,5 Air Ch Hour 1,0 Glazing SF 0,4 Q I 0 W m -2 Glazing 0,7 Illuminance Light transm. levels 300 lux U walls* 3,6 Light power 12 W m -2 U glazing* 4,9 AC efic. 10,4 kj W -1 U roof * 2,0 Mass factor 0,5 Absorption 0,5-1 * U values in W m -2 C The study results showed that air conditioning consumption was very low in this situation and, surprisingly, heating was very high (fig. 8). Figure 8: Example building s simulation results The tolerance for heating was 4 C and neutral temperatures in Sao Paulo are around 24 C, which means a heating base temperature of approximately 19 C. That may be too high a standard for Brazil, where most houses don t have heating and a temperature of 14 C to 15 C is perceived as cold but acceptable. Moreover, this room has no internal loads at all and those are important heat sources in most buildings. However, it can still be noted how heating consumption was not as affected by the obstruction as the others. There was only a 10% increase. That is because most heating usage takes place at night, when obstruction helps to avoid long-wave heat losses to the sky. This compensates a bit for the obstruction in the early morning. Since the comparative results were reasonable, parameters for calculating heating were kept. It was shown that, for São Paulo s climate, cold stress should be a concern in spaces with low internal loads. It was also clear that obstruction influence on this case is stronger in the lighting consumption, which drives the 31% increase in total result.

5 PLEA The 22 nd Conference on Passive and Low Energy Architecture. Beirut, Lebanon, November /5 4.2 Some further studies In order to evaluate cell parameters influence on energy consumption, some more studies were carried on using the 3x3x3 m room. Most parameters were kept from previous simulations, verifying mainly the influence of wall window ratios and internal loads changes. The simulations were made for unobstructed test cells in cardinal orientations. The fig. 9 shows some comparative results. Energy consumption (MWh/year) 120,0 90,0 60,0 30,0 0,0 91,7 85,1 88,6 58,9 56,1 63,8 107,3 105,4 113,5 Lighting Heating Air conditioning Figure 9: Graph showing the performance of unobstructed test cells varying WWR and Q I Energy consumption on unobstructed sites depends much on internal loads. The optimised heating-cooling solution for Sao Paulo s climate was reached at 17 W/m². Lighting in this case was not much of a problem, even though consumption was already high with a WWR. It can be concluded, also, that in unobstructed sites, balanced glazing and opaque areas lead more efficient solutions. Taking the most efficient case as an example, it is possible to evaluate obstruction effect on performance. The angle was increased from 0 to 75 in a 15 step. The results are shown in fig. 10. Energy consumption (kwh/year) 80,0 70,0 60,0 50,0 40,0 30,0 20,0 10,0 0, Obstruction angle ( ) Air Conditioning Heating Lighting Total Figure 10: Relationship between obstruction angle and energy consumption for a 3x3x3 m test cell with 0,5 WWR and a 17 W/m² internal load. One of the first conclusions that can be drawn from this graph is that the behaviour of the total energy consumption is determined by the increase in lighting. It also can be noted that the line is quite flat until the 45 mark and then it starts to rise at a growing rate. Air conditioning reduction tends to get more significant between 30 and 60, but it does not compensate for the lighting consumption after 45. Heating is an almost flat line and does not get much affected by obstruction. Reasons for that were discussed in the previous item. 5. CONCLUSIONS This method proved to be valid for studying solar access in tropical city. It accounts for overheating problems and it points towards integrated approach on the matter. Some more testing, however, should be done in order to evaluate obstruction effects on different kinds of buildings. Data from the actual urban system, and from the constructions composing it, should be gathered, adding precision to the model. Even though there is much to be done, it is already possible to state that solar access (or protection) should be a concern in urban planning for tropical cities. 6. ACKNOWLEDGEMENT We would like to thank IAG-USP, (MASTER and Laboratório de Micrometeorologia) for the climatic data and CNPq for financing this research. REFERENCES [1]J. D. Kendrick, Guide to recommended practice of daylight measurement, CIE, Vienna (1989). [2], A. Brunger, F. Hooper, Anisotropic sky radiance model based on narrow field of view measurements of short-wave radiance. Solar Energy, Pergamon. v. 51, n.1 (1993) [3] N. Igawaa, H. Nakamura, All Sky Model as a standard sky for the simulation of daylit environment. Building and Environment, Pergamon, v. 36, (2001) [4] B. Givoni, Passive and low energy cooling of buildings. John Wiley & Sons. New York. (1994) 263 [5] R. BRANDÃO,. Acesso ao sol e à luz natural: avaliação do impacto de novas edificações no desempenho térmico, luminoso e energético do seu entorno. São Paulo, USP (2004) 156 [6] R. G. Hopkinson, P. Petherbridge, J. Longmore, Iluminação Natural, Fundação Calouste Gulbenkian. Lisboa (1975) 776 p. [7] I. Frame, S. Birch. Daylight Software. Anglia Polytechnic, Londres, (1991) Software [8] A. Frota, S. Schiffer, Manual de conforto térmico, São Paulo, Nobel, (1995) 243 [9] M. Alucci, Conforto térmico, conforto luminoso e conservação de energia elétrica: procedimentos para desenvolvimento e avaliação de projetos de edificações, São Paulo, USP (1992) 225 [10] RORIZ, Maurício. (2001) Consumo de energia no condicionamento térmico de edificações.; um método de avaliação. Anais 6 ENCAC, São Pedro, ANTAC (2001) CD-ROM