5 years ago

Solar Access in Tropical Cities

Solar Access in Tropical Cities

PLEA2005 - The 22 nd

PLEA2005 - The 22 nd Conference on Passive and Low Energy Architecture. Beirut, Lebanon, 13-16 November 2005 2/5 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. 83004), 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 (∆Tinst), using a simple energy balance equation, adapted from Frota & Schiffer [8]: ∆Tinst = (QSO + QST + QLW + QI )/qC + qV [eq. 1] Where: ∆Tinst is the temperature difference from outside; QSO is the solar gain through opaque walls; QSG is the solar gain through glazing; QLW is the long-wave radiation exchange with the sky; QI are the internal gains; qC is the temperature difference heat exchange coefficient; qV is the ventilation heat exchange coefficient. This temperature difference is added to the hourly external temperature, giving the instantaneous internal temperature Tinst = Te + ∆Tinst [eq. 2] An average of the last 24 instantaneous values is then calculated (Tim), in order to determine the behaviour of very heavy construction. The “supposed internal temperature (TSI)”, 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, TIS is determined by the following equation: TSI = m · Tim + (m-1) · Tinst [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 (LC) and heating (LH) loads are given by: L(C or H) = DH (C or H) · q [eq. 4] where q is the sum of qC and qV. 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

PLEA2005 - The 22 nd Conference on Passive and Low Energy Architecture. Beirut, Lebanon, 13-16 November 2005 3/5 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 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). MAD PLAN Mean point of the AD edge PERSPECTIVE Figure 3: Test cell localization in the software 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. SITE DATA (X,Y) ABSOLUTE ENERGY REDUCTION BUILDING DATA (X,Y) BUILDING HEIGHT PERCENTUAL ENERGY REDUCTION ENERGY CONSUMPTION GRAPHS FAÇADE ENERGY REDUCTION Figure 4: Sheet 1 in the Obstruction 1.0 software: data input The basic inputs are site and building coordinates and building height. 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. Sheet 1 Data input Sheet 2 Visualization Sheet 3 Obstructed zones calculations Sheet 4 Radiation calculations Sheet 5 Temperature and air conditioning Sheet 6 Daylighti calculations Sheet 7 Lighting INPUT OUTPUT Site dimensions Building dimensions Obstructede sky zones Site dimensions Building dimensions Obstructed sky zone Adjacent façade azimuth Sky radiance distribution Obstructed and unobstructed irradiance on adjacent façades Obstructed and unobstructed horizontal irradiance Air temperature Sky emissivity Test cell characteristics • Dimension • Thermal properties • Occupation • Air conditioning efficiency Obstructed sky zones Adjacent façades azimuth Sky luminance distribution Iluminância nas fachadas adjacentes com e sem obstrução Test cell characteristics • Dimensions • Luminous properties • Artificial lighting system *Note that energy consumption here always refers to the neighbor buildings Air conditioning energy variation* Heating energy variation Lighting energy variation* Final energy consumption variation Visualization Obstructede sky zones Adjacent façades azimuth Obstructed and unobstructed irradiance on adjacent façades Obstructed and unobstructed horizontal irradiance Unobstructed and obstructed irradiance on tilted surfaces Air conditioning energy variation Heating energy variation* Obstructed and unobstructed illuminance on adjacent façades Lighting energy variation** 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). Figure 6: Obstruction visualization in the software

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