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Development of a typical meteorological year based on dry bulb temperature and dew point for passive cooling applications
Energy for Sustainable Development 33 (2016) 61–74
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Energy for Sustainable Development
Development of a typical meteorological year based on dry bulb temperature and dew point for passive cooling applications Nasser A. Al-Azri Department of Mechanical and Industrial Engineering, Sultan Qaboos University, PO Box 33, Al-Khoud 123, Muscat, Oman
a r t i c l e
i n f o
Article history: Received 22 August 2015 Revised 9 May 2016 Accepted 9 May 2016 Available online xxxx Keywords: TMY Bioclimatic chart Passive cooling
a b s t r a c t Bioclimatic charts are used in assessing the effectiveness and selection of the passive cooling. These charts are based on the typical dry bulb temperature and humidity extracted from the typical meteorological years (TMY) at a given location. TMYs are usually developed for general use and in many situations, they are unavailable due to the lack of some of the weather parameters, especially solar radiation. Dry bulb temperature and dew point measurements are of the most commonly available parameters. This paper presents the development of a typical year based on dry bulb temperature and dew point only from a readily available database. Such an approach will make developing bioclimatic charts more accessible and also more accurate. TMYs, based on dry bulb and dew point temperature, were developed for 12 locations and bioclimatic charts were developed and compared to charts based on common TMYs. © 2016 International Energy Initiative. Published by Elsevier Inc. All rights reserved.
Introduction Passive cooling techniques are used for maintaining comfort at minimal mechanical interaction at certain weather conditions. The most viable passive cooling techniques for a given location are determined with the use of bioclimatic charts (Givoni, 1992, 1994). These charts outline each passive cooling method in a zone overlaid on a psychrometric chart. For a given location, the relevant climatic parameters of a typical meteorological year are projected on the psychrometric chart (Al-Azri et al., 2013). The most important factor in the development of a bioclimatic chart is the availability of a historical record of weather readings, from which a representative typical year can be developed. Typical meteorological years are usually available for general uses and they are developed using many weather parameters that might not be all of interest in some specific applications. For instance, the typical meteorological year (TMY) developed by Sandia method (Hall et al., 1978) is based on wind speed, dry bulb temperature, wet bulb temperature and solar radiation while bioclimatic charts are mainly dependent on dry bulb temperature and humidity. Most often, what makes typical years hardly available is the scarcity in some weather data required for their development. Solar radiation, for example, is one of the most demanding parameters due to the complexity and cost of measurement methods compared to other parameters. In many such cases, solar measurements are interpolated from nearby locations (Al-Sulaiman and
E-mail address: [email protected].
Ismail, 1997) or are developed from daily or monthly averages instead of using hourly measurements (Al-Rawahi et al., 2013; Habte et al., 2014). This paper discusses the development of a typical year tailored for passive cooling purposes and by using only the relevant parameters which are dry bulb temperature and dew point temperature. Avoiding redundant parameters will not only yield a better representation; it will also make the development of the bioclimatic charts more accessible since dry bulb temperature and wet bulb temperature are readily available from different sources. For example, the National Climatic Data Center (NCDC) of the American National Oceanic and Atmospheric Administration (NOAA) maintains a very comprehensive and freely available archive of worldwide stations.
Typical meteorological year Typical meteorological years (TMY) are very commonly used in energy simulation for quantifying the impact of climatic variables on HVAC and energy systems. The first collection of TMY's was developed for 229 locations in the United States using records gathered between 1948 and 1980. The original method used in constructing TMYs was developed by Sandia National Laboratories (Hall et al., 1978). Different studies showed that TMYs developed by Sandia method give reasonable representation of average climatic behavior (Freeman, 1979). This method was adopted by the National Renewable Energy Laboratory (NREL) in the USA for the development of the subsequent generations of TMYs, known as TMY2 (Marion and Urnab, 1995) and TMY3 (Wilcox and
http://dx.doi.org/10.1016/j.esd.2016.05.001 0973-0826/© 2016 International Energy Initiative. Published by Elsevier Inc. All rights reserved.
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N.A. Al-Azri / Energy for Sustainable Development 33 (2016) 61–74
Marion, 2008), with little modifications and has also been widely adopted worldwide. Sandia method is an empirical approach in which individual months are treated separately and the best representative month will be selected from the historical records, e. g., one January will be selected from all Januarys in the historical records. Then the twelve selected months are concatenated and smoothed at the interface in order to make up the full typical meteorological year. Initially, for each month of the year, five candidate months are selected which are those having the least absolute difference between the short term mean daily cumulative distribution function (CDF) and the long term cumulative distribution function (CDF), which is the average of all mean daily values of all years in the record for that month. This, so-called Finkelstein-Schafer statistics (Finkelstein and Schafer, 1971), difference is given by: FS ¼
Fig. 1. Olgyay's bioclimatic chart (Olgyay, 1963).
1 n ∑ δ n i¼1 i
ð1Þ
where n is the number of days in the month and δ is the absolute difference between the long and short term CDFs of day i. Finkelstein–Schafer statistics (FS) is calculated for a number of parameters used in the selection of the typical month in the TMY. The closeness of the short and long term cumulative distribution functions is based on a weighted sum (WS) of those parameters given by: m
ð2Þ
WS ¼ ∑ j¼1 w j FS j
where w is the weight of parameter j and is selected such that ∑m j = 1 w j = 1. The choice of the parameters and the weights is one of the most common differences in the application of Sandia method. The original weights adopted in Sandia methods were mean solar radiation and the mean, maximum, minimum and range of dry bulb temperature, dew point and wind speed. The same weights were adopted in many applications of the method, e.g. that of Petrakis et al. (1996), but there are also many other works in which these weights were reallocated. Pissimanis et al. (1988), as an example, developed the TMY for Athens, Greece in 1988 in which they assigned no weight to the range parameters and the minimum wind velocity. In their development of TMYs for some sites in Oman, Sawaqed et al. (2005) maintained all parameters of Sandia except the minimum wind velocity and they also replaced the dew point temperature with the relative humidity. Moreover, in the subsequent generations of TMY in the United States, (TMY2/ TMY3) (Marion and Urnab, 1995; Wilcox and Marion, 2008), the direct radiation was added to the list of parameters and all
ranges were removed in addition to the minimum wind velocity. Table 1 shows the different weights in the abovementioned works along with those used in Sandia (Hall et al., 1978). These weights will be used later for the sake of comparison in this paper. Overall, the choice of the parameters and their weights is arbitrarily and is based on the discretion of the developers based on their experience with the targeted use of the typical year. The weighted sum, given in Eq. (2) is calculated for the 12 months in each year in the record. For each month in the year, five candidate years from the record will be selected which correspond to the least weighted sums. The five months will be further screened so that the typical month will be one of the five candidates. In the screening process, firstly, the five candidates are sorted in an ascending order based on their closeness to the long term mean and median of the two parameters: mean ambient temperature and global radiation. Then, the persistence of the mean dry bulb temperature and daily horizontal global radiation is tested. For each candidate month, the number of runs and run lengths are counted for the mean daily dry bulb temperatures that are less than 33rd percentile or greater than 67th percentile of the long term mean and also for the mean daily global radiations that are less than 33rd percentile of the long term mean. Preference in the selection is given to candidates having least number of runs and shortest run lengths. Rigorous details on the development of typical meteorological years can be found in the work of Sawaqed et al. (2005).
Table 1 The different weights used in different works in the development of the TMY. Parameter
Sandia (Hall et al., 1978)
Pissimanis et al. (1988)
TMY2/3 (Marion and Urnab, 1995; Wilcox and Marion, 2008)
Sawaqed et al. (2005)*
Max dry bulb temp Min dry bulb temp Mean dry bulb temp Dry bulb temp range Max dew point temp Min dew point temp Mean dew point temp Dew point temp range Max wind velocity Min wind velocity Mean wind velocity Wind velocity range Global radiation Direct radiation
1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 12/24 0
1/24 1/24 2/24 0 1/24 1/24 2/24 0 2/24 0 2/24 0 12/24 0
1/20 1/20 2/20 0 1/20 1/20 2/20 0 1/20 0 1/20 0 5/20 5/20
1/22 1/22 1/22 1/22 1/22 1/22 1/22 1/22 1/22 0 1/22 1/22 11/22 0
*The relative humidity is used in place of dew point temperature.
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Passive cooling Passive cooling is cooling with minimal mechanical interaction and it has been used in architecture since the times of ancient civilizations. Most passive cooling methods are based on three strategies: mass effect, air movement and evaporative cooling (Szokolay, 2004). High mass In hot climates, where diurnal range in temperature is wide enough (exceeds 6 oC), external walls can be made thick enough to delay the flow of heat into the building by as much as 12 h (Reardon, 2010). High mass with night ventilation is very common in hot dry climates where the diurnal difference can exceed 10 oC. Quantitatively, thermal mass is the density times the specific heat (ρ .Cp) measured in units of (kJ/m3·K) or (Btu/ft3·R). The effectiveness of the high mass is dependent on the thermophysical properties of the material used in the construction of the walls (Reardon, 2010). For an effective heat storage, density (ρ), thermal heat capacity (Cp) and thermal conductivity (k) are chosen such that the thermal diffusivity (k/ρCp) is minimum. Porous materials with low specific heat maintain low thermal mass. Good thermal conductivity and low reflectivity are also required for more effective passive cooling when thermal mass is used (Santamouris and Asimakopoulos, 1996). In addition to the abovementioned thermophysical properties, the effectiveness of the thermal mass also depends on the thickness of the building envelope. When high thermal mass is targeted, the thickness of the wall has to be economically justified through optimal tradeoff between capitalizing on wall thickness and energy saving. Ventilation ASHRAE (2009) defines ventilation air as the air used for providing quality indoor air. In night ventilation, the heat accumulated during the daytime is removed by allowing cold air to enter the building. Cold air replaces hot air naturally using wind and stack effect (thermal buoyancy). This cooling process is based on convective heat transfer from the exposed building envelope (Breesch and Janssens, 2007, 2010). Night ventilation is most effective in moderate to hot climates that have significant diurnal temperature difference and low humidity (Kolokotroni, 1995).
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Table 2 The weights used in the development of the typical meteorological year for passive cooling. Parameter
Dry bulb temp.
Dew point
Maximum daily value Minimum daily value Mean daily value Daily range
1/10 1/10 2/10 1/10
1/10 1/10 2/10 1/10
Wind provides cooling through air circulation and evaporative cooling. It is applicable in all types of climates but not as effective in tropical climates at high humidity. At moderate temperatures, a wind blowing at a fraction of meter per second can have a cooling effect equivalent to a few temperature degrees when humidity is around 50% (Santamouris and Asimakopoulos, 1996). At higher humidity, wind speed has to be more for the same cooling effect. In regions where there is a reasonable difference between day and night temperatures, night ventilation, carried out naturally or by mechanical means, is used for cooling in order to tolerate the daytime heat gain and also absorb the heat gain of the next day (Shaviv et al., 2001). Evaporative cooling In evaporative cooling, heat is eliminated through the evaporation of water. In direct evaporative cooling, hot air stream is passed over a wet structure where some of the heat is lost to the evaporating water which results in a higher moisture content. In direct evaporative cooling, dry bulb temperature is decreased while relative humidity is increased. In indirect evaporative cooling, dry bulb temperature is decreased without increasing the moisture content. In this method, hot stream is first cooled using direct evaporative method and then it is used to cool the room air in a heat exchanger without direct contact. Evaporative cooling is mostly effective in dry climates where there is enough difference between dry and wet bulb temperatures. Thermal comfort Human thermal comfort is a relative quality which is subject to individual, climatic and cultural differences and hence cannot be defined accurately. For instance, Givoni (1992) mentioned that people living in
Fig. 2. Typical building bioclimatic chart developed by Givoni (1992).
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Table 3 The 12 locations used in the development of the typical year for passive cooling.
1 2 3 4 5 6 7 8 9 10 11 12
Location
Station name
WMO station ID
Latitude
Longitude
Elevation (m)
Bahrain Cairo, Egypt Christchurch, New Zealand Dakar, Senegal Dubai, UAE Jeddah, Saudi Arabia Kuwait City, Kuwait Muscat, Oman Napoli, Italy New Delhi, India Singapore, Singapore Waco, Texas, USA
Bahrain Int. AP Cairo Int. AP Christchurch Int. AP Leopold Senghor Intl. AP Dubai Int. AP King Abdulaziz Int. AP Kuwait Int. AP Muscat Int. AP Capodichino Safdarjung AP Singapore Changi Int. AP Waco Reg. AP
411500 623660 937800 616410 411940 410240 405820 412560 162890 421820 486980 722560
26.271 30.122 −43.489 14.740 25.255 21.680 29.227 23.595 40.85 28.585 01.350 31.619
50.634 31.405 172.535 −17.490 55.364 39.157 47.969 58.298 14.30 77.206 103.994 −97.228
1.8 116.4 37.5 25.9 10.4 14.6 62.8 8.0 72.0 214.9 6.7 152.4
naturally ventilated, unconditioned buildings usually have more tolerance to changes in the temperature of summer days. MacPherson (1973) defined six factors that can affect the sensation of thermal comfort which are: air speed, mean radiant temperature (MRT), metabolic rate, temperature, humidity and the level of clothing. Fanger (1972) suggested a model that takes into account the human factors, namely the activity level and clothing level, in addition to the climatic variables. His so-called Predicted Mean Vote (PMV) model was developed experimentally from the responses of college-age subjects set in a uniform environment at steady conditions. As its name suggests, this model is developed from the predicted mean of votes raised by a large population of people expressing their thermal sensation in a given environment. Because of the many governing variables affecting comfort, there has not been a definite quantitative expression of a comfort zone. In the
Table 4 The candidate years for Muscat, Oman. Ranked by minimum WS Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
13 5 11 6 12 11 3 1 1 10 9 14
14 10 6 1 14 13 7 10 6 3 14 12
3 14 9 12 6 9 5 13 10 6 11 2
9 9 7 2 9 2 8 12 8 2 4 9
Ranked by persistence criteria 14 10 7 6 4 13 8 13 5 3 14 9
5 3 3 8 4 5 12 11 5 12 12 5
13 14 3 8 12 9 5 12 8 10 9 14
9 3 6 2 6 2 7 11 1 6 11 5
3 5 9 12 14 11 3 1 6 2 4 12
5 9 11 1 9 5 12 10 10 12 12 2
PMV model, comfort is measured qualitatively using a seven-point index scale which is the same as ASHRAE (2009) scale (cold, cool, slightly cool, neutral, slightly warm, warm and hot). However, the quantitative scaling of this method ascends from − 3 to 3 instead of ASHRAE's scale that goes from 1 to 7. In another model developed by MacPherson (1973), a 19-grade index is developed which is a function of one or more of the six factors impacting the level of comfort. In spite of the many variables that decide thermal comfort, there is a range of temperature and humidity values within which the vast majority of people feel satisfied. These ranges are usually used to define the thermal comfort zone in a psychometric chart. Different literature works might slightly vary in outlining the boundaries of a thermal zone, but they all agree on a vast common overlap. The comfort zone set by ASHRAE (2009) allows no consideration to acclimatization to different climates. According to ASHRAE Standard 55, thermal neutrality is defined as the indoor thermal index value for which a mean vote of neutral is expressed on the thermal sensation scale. Earlier works, e.g. Humphreys (1978) expressed thermal neutrality in terms of temperature only and it was correlated to the external air temperature. Comfort was assumed to be achieved at these temperature values provided that other factors such as humidity and clothing are satisfied. Building bioclimatic charts Bioclimatic charts offer a convenient way for predicting, whether or not, passive cooling is likely to improve thermal comfort in a building. The selection of a passive cooling strategy is heavily based on the local climatic conditions. Olgyay's (1963) bioclimatic chart (Fig. 1), developed in the 1950s, was one of the early attempts used for specifying different strategies on a dry bulb temperature versus relative humidity chart. Oglyay's
Table 5 The years used in the construction of the typical meteorological year.
1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12.
Location
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Bahrain Cairo Christchurch Dakar Dubai Jeddah Kuwait Muscat Napoli New Delhi Singapore Waco
2002 2006 2006 2006 2014 2009 2013 2007 2011 2013 2005 2002
2002 2006 2008 2013 2004 2004 2013 2001 2004 2004 2011 2001
2008 2006 2009 2001 2007 2006 2006 2007 2011 2001 2002 2002
2011 2005 2000 2003 2006 2008 2014 2003 2010 2000 2009 2003
2008 2005 2003 2002 2014 2013 2009 2004 2005 2009 2006 2010
2005 2003 2007 2000 2006 2007 2007 2001 2007 2011 2014 2004
2008 2002 2002 2006 2005 2012 2005 2009 2009 2013 2009 2010
2008 2009 2014 2009 2008 2013 2004 2005 2006 2011 2010 2000
2004 2012 2001 2013 2003 2006 2010 2001 2010 2010 2002 2002
2003 2006 2011 2008 2002 2011 2004 2009 2003 2000 2004 2008
2002 2002 2010 2013 2009 2013 2010 2000 2008 2002 2009 2008
2007 2013 2012 2011 2014 2007 2004 2002 2009 2003 2001 2011
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Table 6 Typical years for each month in Muscat, Oman, using different development approaches. TMY approach
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Sandia (Hall et al., 1978) Pissimanis (Pissimanis et al., 1988) TMY2/3 (Marion and Urnab, 1995; Wilcox and Marion, 2008) Sawadeq (Sawaqed et al., 2005) This work
2009 2009 2005 2009 2007
2012 2002 2002 2012 2001
2007 2007 2011 2007 2007
2014 2014 2007 2014 2003
2013 2013 2013 2013 2004
2012 2012 2012 2012 2001
2010 2010 2010 2010 2009
2009 2009 2009 2009 2005
2005 2005 2001 2005 2001
2002 2002 2006 2005 2009
2012 2012 2012 2012 2000
2007 2007 2007 2007 2002
chart outlines the comfort zone between 20 and 30 °C. Thermal comfort in indoor spaces is assumed with indoor level of clothing and it is shown at the center of the chart. The chart also shows the effect of other climatic factors such as mean radiant temperature, wind speed and solar radiation on thermal comfort. Slightly above the lower boundary of the zone, shading is necessary for maintaining reasonable level of
comfort. Up to 10 °C below the comfort zone, comfort can be retained provided that there is enough solar radiation in order to offset the decrease in temperature. Similarly, in order to retain comfort within 10 °C above the comfort zone, wind speed can also offset the increase in temperature. Evaporative cooling, as suggested by Olgay, can retain comfort at high temperature values but low humidity.
Fig. 3. (a) The monthly mean dry bulb temperature for the different years in the record and the typical meteorological year. (b) The monthly mean dry bulb temperature for the different years in the record and the typical meteorological year.
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N.A. Al-Azri / Energy for Sustainable Development 33 (2016) 61–74
Fig. 3 (continued).
Since Olgyay's chart only considers outdoor conditions, it is only applicable for hot humid climates where there is minimal fluctuations between the indoor and the outdoor temperatures which limit its application for low-mass ventilated buildings (Sayigh and Marafia, 1998). A more popular bioclimatic chart is that of Givoni (1992, 1994). The development of Givoni's chart was based on experimental research on buildings in Europe and USA. These buildings were characterized by their low heat gain and thereby the reliability of the charts for high internal gain is questionable (Givoni, 1994; Lomas et al., 2004). Hence, it is mostly applicable for residential and office buildings where heat gain is minimal. Givoni's chart is based on the linear relationship between the temperature amplitude and vapor pressure of the outdoor air (Sayigh
and Marafia, 1998). It identifies the suitable passive cooling technique based on the outdoor climatic condition assuming minimal internal heat gain. Five zones are identified on Givoni's chart which are: thermal comfort, natural ventilation, high mass, high mass with night ventilation and evaporative cooling. Givoni's chart (Fig. 2) is laid on the psychrometric chart and preserves the original psychrometric parameters which are used in the selection of passive cooling strategies. Modifications to Givoni's chart that suit non-domestic buildings can also be found in the literature (Lomas et al., 2004). The natural ventilation zone on Givoni's chart assumes that the indoor mean radiant temperature and the vapor pressure are similar to those at outdoor conditions; which is also an assumption that limits the application of the chart to buildings having medium to high thermal mass (Watson, 1981).
N.A. Al-Azri / Energy for Sustainable Development 33 (2016) 61–74
On the same chart, at high temperatures, mechanical air-conditioning is necessary to keep a habitable environment while on the left of the comfort zone (CZ), heating is needed for restoring comfort using either solar or mechanical heating. The high thermal mass effect is provided using heavy construction that absorbs heat to be reversed in direction overnight (Szokolay, 2004). In hot, dry climates, night ventilation is used in releasing heat through windows, assisted by fans if necessary. Due to its simplicity and preservation of all psychrometric parameters, Givoni's charts are an attractive means for quantifying the applicability of passive cooling strategies. Construction of bioclimatic charts can be found in more rigorous details in the literature (Al-Azri et al., 2013).
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Methodology In the determination of passive cooling methods as in the abovementioned bioclimatic chart, the main parameters are the drybulb temperature and the relative humidity which can also be evaluated using dry-bulb temperature and dew point. Hence dry bulb temperature and dew point are used in the development of a representative typical year for developing bioclimatic charts. Tailoring the representation to bioclimatic charts, not only will allow better representation of the typical year for this purpose, it will also be more accessible since records of hourly dew point and dry bulb temperatures are more common than other parameters.
Fig. 4. (a) The monthly mean dew point temperature for the different years in the record and the typical meteorological year. The monthly mean dew point temperature for the different years in the record and the typical meteorological year.
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N.A. Al-Azri / Energy for Sustainable Development 33 (2016) 61–74
Fig. 4 (continued).
The development of the typical year will follow Sandia procedure (Hall et al., 1978) but by only considering dry bulb temperature and dew point. The short term cumulative distribution functions are constructed for the mean values of maximum, minimum, mean and range of daily readings for both dry bulb and dew point temperatures. The long term CDF will be the average of all of these parameters over the years. Finkelstein–Schafer statistics will be calculated using the weights shown in Table 2 of the four parameters. After selecting five candidates for each month, they will be ranked based on their closeness to the long term mean and median using the mean values of the two variables as mentioned in the description of Sandia method. When evaluating the persistence of mean values using
the frequency and run length; for both mean daily dry bulb temperature and mean daily dew point temperature, values below 33rd percentile and above 67th percentile of the long term mean values will be considered. Then the same run length and number of runs criteria are used in the ultimate selection of each month.
Meteorological data The development of the typical year was carried out for 12 locations chosen from different countries around the world. The geographical locations and weather station IDs are shown in Table 3.
N.A. Al-Azri / Energy for Sustainable Development 33 (2016) 61–74
The typical years using the two essential parameters, dry bulb and dew point, were developed for the 12 locations. The variation with the regular typical meteorological year in the application of passive cooling techniques was tested graphically and numerically based on the zones outlined by Givoni (1992). Typical meteorological years for all the locations were taken from Meteonorm 7.0 database developed using solar and meteorological ground measurements. The hourly weather data records for all locations except Muscat, Oman, was extracted from the National Climatic Data Center (NCDC) of the American National Oceanic and Atmospheric Administration (NOAA) which maintains world record in a well-maintained database. The data was claimed online from the database and it exits in a somewhat crude format as received from the original source and hence required further processing to be valid as input to the development of the typical year. The data is provided in html files with 12 files per
69
year and it includes the measurement as provided by the station. Hence, there are many gaps and in many situations readings were taken at fractions of an hour or every few hours rather than every 60 min. A computer code was developed to read the files and reorganize them in a handier format. The data, which originally exists at Greenwich Mean Time, were extracted for 15 years, from 2000 to 2014 and shifted to the local time. Each year was finally made up of 8760 h. The readings of the 29th of February in leap years were ignored. In the handling of the original data, linear interpolation was used to fill small gaps up to 5 h. Gaps that are longer than 5 h were filled by linear interpolation using the existing data of closest days and matched to the same hours of the day. Closer days to the missing points were given higher weights than further days. When in the rare cases, the gap extends to more than a month; interpolation is performed with the neighboring years using existing data at the
Fig. 5. (a) The bioclimatic charts developed using normal TMY (solid lines) and the TMY developed for passive cooling (dashed lines). (b) The bioclimatic charts developed using normal TMY (solid lines) and the TMY developed for passive cooling (dashed lines).
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N.A. Al-Azri / Energy for Sustainable Development 33 (2016) 61–74
Fig. 5 (continued).
same period of the year. After interpolation, points at the interface of the interpolated period were smoothed if the difference with the neighboring measured reading is greater than 3°C for both dry bulb temperature and dew point temperature. Smoothing was performed using linear regression with two points at each side of the interface. This routine for dealing with the missing data has shown very good consistency with the daily and seasonal fluctuations. Except for Bahrain and Saudi Arabia whose missing points are around 8%, missing points at all other stations are less than 5% with seven stations being around 1% or less. Discussion The modified Sandia method was implemented with the weights given in Table 2. First, five candidates were selected based on the
difference between the short and long term cumulative distribution functions. The five candidates were then ranked based on the absolute differences between short and long term means and medians for both dry bulb and dew point temperatures. The candidate having widest difference will have lesser chance to be selected. In the final screening, the persistence of mean values is evaluated by testing by calculating the frequency and run length above the 67th long term percentile and below the 33rd long term percentile. The number of runs and their lengths are calculated and then in a selective procedure, preference is given to candidates having less number of runs and shortest run length. The different screening is used in order to ensure that while maintaining minimum difference between short and long term CDFs, preference is given to the set that has good distribution around the mean and median.
N.A. Al-Azri / Energy for Sustainable Development 33 (2016) 61–74
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the persistence criteria. The underlined years are those having the finally selected typical candidate after performing the run counts and length criteria.
Table 4 shows an example of the selection criteria for Muscat, Oman from the candidate years (1:15) corresponding to (2000: 2014) using minimum weighted sum (WS) of Finkelstein–Schafer statistics and
Table 7.a The percentage of monthly hours that fall on each passive cooling zone based on normal TMY (A) and the developed TMY for passive cooling (B).
Cairo, Egypt M
CZ
NV
Christchurch, New Zealand
HM
HMV
EC
CZ
NV
HM
HMV
EC
1
8
9
8
9
8
10
8
10
8
10
23
22
25
23
24
25
24
25
24
26
2
12
14
13
14
13
15
13
15
13
16
15
16
17
17
16
18
16
18
16
18
3
32
29
36
31
37
38
37
38
37
40
11
13
11
13
11
13
11
13
11
13
4
49
38
60
47
61
54
61
54
61
55
3
4
3
4
3
4
3
4
3
4
5
45
48
78
70
83
75
85
78
76
77
1
4
1
4
1
4
1
4
1
4
6
35
21
83
76
81
75
86
83
66
53
0
0
0
0
0
0
0
0
0
0
7
9
0
76
66
40
13
48
32
17
3
0
0
0
0
0
0
0
0
0
0
8
5
1
77
77
28
32
34
41
7
5
0
1
0
1
0
1
0
1
0
1
9
29
19
90
90
72
68
73
70
47
46
1
2
1
2
1
2
1
2
1
2
10
47
48
89
89
74
83
74
85
64
75
3
1
3
1
3
1
3
1
3
1
11
42
49
46
53
45
54
45
54
45
54
7
13
7
14
7
14
7
14
7
14
12
15
16
15
19
15
19
15
19
15
19
19
12
19
13
19
14
19
14
19
14
A
B
A
B
A
B
A
B
A
B
A
B
A
B
A
B
A
B
A
B
Dakar, Senegal M
CZ
NV
Dubai, UAE
HM
HMV
EC
CZ
NV
HM
HMV
EC
1
61
62
68
70
64
63
64
63
63
63
41
50
44
50
43
51
43
51
42
51
2
44
55
59
71
51
61
51
61
49
64
50
61
59
65
58
66
58
66
58
67
3
43
25
69
64
49
34
49
34
47
32
56
60
78
78
78
85
79
85
73
84
4
22
21
70
87
24
28
24
28
23
25
41
27
83
80
86
77
92
82
76
72
5
15
6
93
19
10
19
10
17
9
7
1
53
53
58
45
87
63
35
27
6
2
0
91 100
4
2
4
2
3
1
0
0
44
21
10
18
21
47
2
15
7
1
0
96
99 100
5
0
5
0
1
0
0
0
18
11
4
3
11
16
1
1
8
0
0
92
95
0
0
0
0
0
0
0
0
17
5
3
3
16
24
0
2
9
0
0
86
88
0
0
0
0
0
0
0
0
43
21
5
7
11
21
0
4
10
0
0
90
94
0
4
0
4
0
2
4
1
71
64
36
21
47
29
13
12
11
9
9
99
99
27
30
27
30
16
15
52
33
94
91
93
75
93
76
79
61
12
52
44
95
89
74
69
74
69
66
72
57
73
70
82
68
83
68
83
66
84
A
B
A
B
A
B
A
B
A
B
A
B
A
B
A
B
A
B
A
B
Jeddah, Saudi Arabia M
CZ
NV
Kuwait City, Kuwait
HM
HMV
EC
CZ
NV
HM
HMV
EC
1
57
56
80
89
77
82
77
82
74
76
6
10
6
10
6
10
6
10
6
10
2
49
39
83
78
76
56
76
57
66
50
17
17
17
17
20
20
20
20
21
28
3
35
35
84
84
77
66
79
69
54
54
21
22
24
25
42
39
42
39
52
55
4
23
9
82
78
58
33
63
42
42
21
37
27
52
41
79
70
83
78
87
83
5
8
4
63
63
33
26
47
32
12
9
7
11
23
30
59
62
95
79
84
89
6
2
2
56
54
14
17
25
34
5
6
1
0
8
6
40
25
83
56
59
51
7
0
0
45
34
2
7
17
16
0
0
0
0
5
2
27
21
71
63
43
47
8
0
0
34
18
0
6
1
14
0
3
0
1
11
8
29
31
74
72
40
51
9
0
0
45
34
0
0
0
1
0
0
6
2
26
15
55
32
92
63
81
55
10
1
1
66
64
4
4
5
8
1
2
41
21
66
44
84
55
99
86
97
71
11
11
15
91
89
36
50
37
51
18
33
32
36
42
45
46
53
46
53
45
56
12
42
49
90
90
79
84
79
85
62
76
15
2
16
2
15
2
15
2
15
2
A
B
A
B
A
B
A
B
A
B
A
B
A
B
A
B
A
B
A
B
72
N.A. Al-Azri / Energy for Sustainable Development 33 (2016) 61–74
Table 5 shows the years in the record whose corresponding month has the best representative readings for that specific location based on the weights assigned in this work. The successfully selected 12 were
concatenated to construct the typical year. The data was smoothed at the concatenation interface by using linear regression with 6 h at each side of the interface.
Table 7.b The percentage of monthly hours that fall on each passive cooling zone based on normal TMY (A) and the developed TMY for passive cooling (B).
Manamah, Bahrain M
CZ
NV
Muscat, Oman
HM
HMV
EC
CZ
NV
HM
HMV
EC
1
17
19
17
32
17
19
17
19
17
19
55
59
58
67
56
61
56
61
56
61
2
29
28
30
33
29
28
29
28
29
28
55
59
69
85
66
68
66
68
65
65
3
60
59
67
68
65
71
65
71
64
70
53
43
90
86
84
65
85
65
77
61
4
48
54
95
92
80
89
81
89
69
79
28
14
75
67
78
55
85
65
68
49
5
4
0
63
63
48
51
62
61
18
17
2
1
34
34
43
35
81
65
29
36
6
0
0
40
33
19
12
40
33
3
2
0
0
28
4
11
4
28
34
1
6
7
0
0
16
4
3
3
9
20
0
0
0
0
31
8
0
3
1
12
0
1
8
0
0
5
1
0
0
0
5
0
0
0
0
48
26
1
0
1
3
0
0
9
0
0
38
19
1
3
1
8
0
0
0
0
66
61
2
6
2
11
0
2
10
1
0
73
67
10
12
11
16
3
3
10
8
77
73
53
27
63
31
22
18
11
53
50
92
92
68
66
68
66
63
58
41
30
95
96
74
58
74
58
59
46
12
38
46
42
55
40
47
40
47
39
47
55
39
76
86
65
58
65
58
62
50
A
B
A
B
A
B
A
B
A
B
A
B
A
B
A
B
A
B
A
B
Napoli, Italy M
CZ
NV
New Delhi, India
HM
HMV
EC
CZ
NV
HM
HMV
EC
1
0
0
0
0
0
0
0
0
0
0
8
8
8
8
8
8
8
8
8
8
2
0
0
0
0
0
0
0
0
0
0
28
28
31
32
31
33
31
33
31
32
3
1
1
1
1
1
1
1
1
1
1
39
42
65
61
66
65
67
65
61
63
4
8
9
8
9
8
9
8
9
8
9
34
24
65
59
80
59
97
83
78
51
5
38
43
47
48
42
46
42
46
41
45
11
6
51
51
41
38
64
58
29
29
6
38
34
75
73
52
52
52
52
45
41
1
0
47
38
13
0
18
3
2
0
7
27
32
91
95
64
54
64
55
42
44
0
0
37
27
1
0
2
0
0
0
8
26
37
92
89
55
67
55
69
38
52
0
0
45
45
0
0
0
0
0
0
9
40
50
58
68
48
57
48
57
45
54
0
0
78
74
1
0
1
0
0
0
10
28
22
31
26
29
23
29
23
29
23
24
14
78
76
52
30
53
32
39
22
11
6
11
6
12
6
11
6
11
6
11
37
30
45
47
44
42
44
42
43
39
12
0
0
0
0
0
0
0
0
0
0
17
18
17
18
17
18
17
18
17
18
A
B
A
B
A
B
A
B
A
B
A
B
A
B
A
B
A
B
A
B
Singapore, Singapore M
CZ
NV
Waco, Texas, USA
HM
HMV
EC
CZ
NV
HM
HMV
EC
1
0
0
98
99
0
0
0
0
0
0
8
8
8
10
9
8
9
8
9
8
2
0
0
92
95
0
1
0
1
0
0
10
8
11
13
11
8
11
8
11
8
3
0
0
83
83
0
0
0
0
0
0
17
10
24
21
21
12
21
12
21
11
4
0
0
81
79
0
0
0
0
0
0
30
29
46
52
38
42
38
42
36
39
5
0
0
67
76
0
0
0
0
0
0
17
17
71
73
42
39
42
40
26
23
6
0
0
75
73
0
0
0
0
0
0
12
0
83
87
35
8
36
12
20
0
7
0
0
82
87
0
0
0
0
0
0
9
0
70
74
27
1
31
5
19
0
8
0
0
80
87
0
0
0
0
0
0
10
5
67
61
29
23
37
53
18
8
9
0
0
85
88
0
0
0
0
0
0
23
15
73
76
50
41
52
48
38
24
10
0
0
79
87
0
0
0
0
0
0
23
26
48
45
35
41
35
41
33
40
11
0
0
89
91
0
0
0
0
0
0
17
22
19
24
19
24
19
24
19
26
12
0
0
92
95
0
0
0
0
0
0
7
1
7
3
8
1
8
1
8
1
A
B
A
B
A
B
A
B
A
B
A
B
A
B
A
B
A
B
A
B
N.A. Al-Azri / Energy for Sustainable Development 33 (2016) 61–74
Table 6 shows the typical years for each month using the weights discussed in Table 1. It is apparent that when all parameters in Table 1 are considered, there is little differences amongst the different approaches used. However, when only dry bulb temperature and dew point are considered, the selection is very different. The effect of this difference becomes clearer when the developed TMY is compared to the regular full TMY (e.g. TMY3 approach) with the same historical record of readings. Figs. 3 and 4 show the behaviors of the monthly mean dry bulb temperature and dew point temperature for all years in the record and the constructed typical year for the 12 locations. The mean dry bulb temperature and mean dew point weighs 20% each in the selection of the typical month. Most of the selected typical months have an average that is very close to the average of the mean value on the graph. Other deviations can be explained as a result of the influence of other parameters considered in the weights. Using the developed typical years for dry bulb temperature and dew points, other psychrometric parameters were developed using the equations of thermodynamics for air–water vapor mixture (Al-Azri et al., 2013). Bioclimatic charts were developed for the 12 locations using both the typical meteorological year acquired from Meteonorm 7.0 database and the typical year based on dry bulb temperature and dew point, developed in this work. The applicability of a given passive cooling technique in a given month is usually represented by a line that extends from the pair of average daily minimum dry bulb temperature and average maximum daily humidity to the opposite pair i.e. average daily maximum dry bulb temperature and average minimum daily humidity. The portion of that line segment that falls on a given passive cooling zone indicates the extent to which the passive cooling technique is applicable. Fig. 5a and b shows the bioclimatic charts for the twelve locations. The solid line segments represent each month based on the regular typical meteorological years acquired from Meteonorm 7.0 while the dashed lines are developed from the typical year developed in this work from the historical record of each station. The distribution of both sets of line segments for all locations shows almost similar trends. Moreover, most of the pairs of line segments for a given month lie close to each other. The similarity in the shape of the distribution is expected since dry bulb temperature and dew point usually carry more than 40% of the weight in the selection of the typical year when other parameters such as radiation and wind are also considered.
Fig. 6. Givoni bioclimatic chart for Muscat, Oman using TMY3 approach (Wilcox and Marion, 2008) (solid lines) and the approach developed for passive cooling (dashed lines).
73
Table 8 The percentage of monthly hours that fall into each passive cooling zone in Muscat, Oman based on normal different TMY approaches (A–D) and the developed TMY for passive cooling. Month CZ
NV
HM
HMV
EC
A B C D E A B C D E A B C D E A B C D E A B C D E
1
2
3
4
5
6
7
8
9
10
11
12
31 31 43 31 43 32 32 46 32 45 33 33 47 33 47 33 33 47 33 47 33 33 48 33 48
37 37 38 37 40 45 45 42 45 44 48 48 50 48 49 48 48 50 48 49 49 49 51 49 50
48 48 47 48 47 67 67 65 67 67 80 80 79 80 79 82 82 81 82 81 82 82 83 82 81
21 21 20 21 18 52 52 46 52 47 76 76 75 76 69 98 98 98 98 93 89 89 96 89 79
9 9 9 9 10 47 47 47 47 38 65 65 65 65 68 94 94 94 94 97 69 69 69 69 81
8 8 8 8 8 37 37 37 37 46 53 53 53 53 52 93 93 93 93 90 55 55 55 55 45
0 0 0 0 1 34 34 34 34 32 17 17 17 17 42 41 41 41 41 73 4 4 4 4 21
0 0 0 0 1 48 48 48 48 46 31 31 31 31 48 54 54 54 54 78 19 19 19 19 32
11 11 20 11 21 56 56 58 56 58 63 63 63 63 63 98 98 96 98 97 53 53 59 53 60
47 47 44 49 44 58 58 66 66 60 78 78 74 83 76 99 96 95 96 96 94 94 78 86 87
45 45 45 45 46 77 77 77 77 78 80 80 80 80 81 80 80 80 80 81 77 77 77 77 75
39 39 39 39 37 49 49 49 49 45 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48
A: Sandia (Hall et al., 1978), B: Pissimanis (Pissimanis et al., 1988), C: TMY2/3 (Marion and Urnab, 1995; Wilcox and Marion, 2008), D: Sawaqed (Sawaqed et al., 2005) E: This work.
However, in many situations variation still exists when each passive technique is considered separately for each month. A more quantifiable comparison is given in Tables 7.a and 7.b. The figures in the table represent the percentage of hours per month falling on the comfort zone (CZ), night ventilation (NV), high mass (HM), high mass with night ventilation (HMV) and evaporative cooling (EC). For each zone, there are two sets, (A) those of the TMY extracted from Meteonorm 7.0 database and (B) from the typical year developed in this work. About 50% of the total pairs maintain a relative difference between the two sets that is 12% or less. On the other hand, 25% of the pairs exhibit a relative difference of 35% or more. There has not been a common trend of variation with respect to graphical location, passive technique or time of the year. However, this amount of variation is expected for the essential reason that set B was developed based on dry bulb temperature and dew point only and is supposed to be a better representative for determining passive cooling techniques based on the bioclimatic chart. Other significant reasons for the variation are the subtle differences between the variant choices made when TMY is built even when the same parameters are considered and also another reason is the fact that the two typical years are developed using two different sets of the historical record. Fig. 6 shows the bioclimatic chart for Muscat, Oman using two approaches in developing the TMY and both are based on the same historical record. The solid lines are based on the approach developed by Wilcox and Marion (2008) in developing TMY3 and it is almost identical to other approaches that considers all parameters as shown in Table 6. The dashed lines are based on the typical year developed based on dry bulb temperature and dew point only. The shape of distribution of the two sets of lines is similar but there are still some variations seen between the pairs of each month. This variation is quantified in Table 8 which shows the percentage of readings that fall within the boundaries of each zone using the regular approaches (A–D) and the approach adopted in this paper (E). In most cases, the differences are insignificant to consider except for a few cases.
74
N.A. Al-Azri / Energy for Sustainable Development 33 (2016) 61–74
Conclusion Typical meteorological year was developed based on dry bulb temperature and dew point for the purpose of determining the applicability of passive cooling techniques based on bioclimatic charts. Typical meteorological years are usually developed for common purposes and are better when tailored for the purpose of use. Limiting the development of the typical year to dry bulb temperature and dew point should give more accurate representation. When compared to regular TMY, the developed one exhibits similar distribution shape on the chart but it exhibits some variations when compared more specifically by month and passive technique. Acknowledgement The author would like to acknowledge the support provided by the Research Council of Oman (TRC) under Grant Number ORG/EI/12/005. References Al-Azri N, Zurigat Y, Al-Rawahi N. Development of bioclimatic chart for passive building design. Int J Sustain Energy 2013;32(6):713–23. Al-Rawahi N, Zurigat Y, Al-Azri N. Prediction of hourly solar radiation on horizontal and inclined surfaces for Muscat/Oman. J Eng Res 2013;8(2):19–31. Al-Sulaiman F, Ismail B. Estimation of monthly average daily and hourly solar radiation impinging on a sloped surface using the isotropic sky model for Dhahran. Saudi Arab Renew Energy 1997;11(52):257–62. ASHRAE. ASHRAE handbook fundamentals. Atlanta, GA: ASHRAE; 2009. Breesch H, Janssens A. Reliable design of natural night ventilation using building simulation. Proceedings of the thermal performance of the exterior envelopes of whole buildings X international conference; 2007. p. 1–14. ASHRAE; Clearwater Beach, Florida. Breesch H, Janssens A. Performance evaluation of passive cooling in office buildings based on uncertainty and sensitivity analysis. Sol Energy 2010;84:1453–67. Fanger PO. Thermal comfort: analysis and applications in environmental engineering. New York: McGraw-Hill; 1972. Finkelstein J, Schafer R. Improved goodness-of-fit tests. Biometrika 1971;58(3):641–5. Freeman T. Evaluation of the typical meteorological years for solar heating and solar cooling system studies. SERI/TR-8150-1 Golden, CO: Solar Energy Research Institute; 1979.
Givoni B. 1992. Comfort, climate analysis and building design guidelines. Energy Build 1992;1:11–23. Givoni B. Passive low energy cooling of buildings. New York: Van Nostrand Reinhold; 1994. Habte A, Lopez A, Sengupta M, Wilcox S. Temporal and spatial comparison of gridded TMY, TDY, and TGY data sets. National Renewable Energy Laboratory; 2014. Hall I, Prairie R, Anderson H, Bose E. Generation of typical meteorological years for 26 SOLMET stations. SAND78–1601 Albuquerque, NM: Sandia National Laboratories; 1978. Humphreys MA. Outdoor temperature and comfort indoor. Build Res Pract 1978;6: 92–105. Kolokotroni M. Night ventilation in commercial buildings. Annex 28: review of low energy cooling technologies subtask 1. Ottawa: International Energy Agency; 1995. p. 7–11. Lomas KJ, Fiala D, Cook MJ, Copper PC. Building bioclimatic charts for non-domestic buildings and passive downdraught evaporative cooling. Build Environ 2004;39:661–76. MacPherson RK. Thermal stress and thermal comfort. Ergonomics 1973;16(5): 1366–5847. Marion W, Urnab K. User's manual for TMY2s typical meteorological years. NREL US Department of Energy; 1995. Olgyay V. Design with climate, bioclimatic approach and architectural regonalism. New Jersey: Princeton University Press; 1963. Petrakis M, Lykoudis S, Kassomenos. A software tool for the creation of a typical meteorological year. Environ Softw 1996;11(4):221–7. Pissimanis D, Karras G, Notaridu V, Garva K. The generation of a typical meteorological year for the city of Athens. Sol Energy 1988;40:405–11. Reardon C. Home technical manual. Australia: Department of Climate Change and Energy Efficiency; 2010. Santamouris M, Asimakopoulos D. Passive cooling of buildings. Routledge; 1996. Sawaqed NM, Zurigat YH, Al-Hinai H. A step-by-step application of Sandia method in developing typical meteorological years for different locations. Int J Energy Res 2005; 29(8):723–37. Sayigh A, Marafia AH. Thermal comfort and the development of bioclimatic concept in building design. Renew Sustain Energy Rev 1998;2:3–24. Shaviv E, Yezioro A, Capeluto IG. Thermal mass and night ventilation as passive cooling design strategy. Renew Energy 2001;24:445–52. Szokolay SV. Introduction to architectural science: the basis of sustainable design. Oxford: Architectural Press; 2004. Watson D. Analysis of weather data for determining appropriate climate control strategies in architectural design. In: Haisley R, editor. Proceedings of the international passive and hybrid cooling conference; 1981. The International Solar Energy Society; Miami Beach, Florida. Wilcox S, Marion W. User's manual for TMY3 data sets. NREL, US Department of Energy; 2008.
Contents lists available at ScienceDirect
Energy for Sustainable Development
Development of a typical meteorological year based on dry bulb temperature and dew point for passive cooling applications Nasser A. Al-Azri Department of Mechanical and Industrial Engineering, Sultan Qaboos University, PO Box 33, Al-Khoud 123, Muscat, Oman
a r t i c l e
i n f o
Article history: Received 22 August 2015 Revised 9 May 2016 Accepted 9 May 2016 Available online xxxx Keywords: TMY Bioclimatic chart Passive cooling
a b s t r a c t Bioclimatic charts are used in assessing the effectiveness and selection of the passive cooling. These charts are based on the typical dry bulb temperature and humidity extracted from the typical meteorological years (TMY) at a given location. TMYs are usually developed for general use and in many situations, they are unavailable due to the lack of some of the weather parameters, especially solar radiation. Dry bulb temperature and dew point measurements are of the most commonly available parameters. This paper presents the development of a typical year based on dry bulb temperature and dew point only from a readily available database. Such an approach will make developing bioclimatic charts more accessible and also more accurate. TMYs, based on dry bulb and dew point temperature, were developed for 12 locations and bioclimatic charts were developed and compared to charts based on common TMYs. © 2016 International Energy Initiative. Published by Elsevier Inc. All rights reserved.
Introduction Passive cooling techniques are used for maintaining comfort at minimal mechanical interaction at certain weather conditions. The most viable passive cooling techniques for a given location are determined with the use of bioclimatic charts (Givoni, 1992, 1994). These charts outline each passive cooling method in a zone overlaid on a psychrometric chart. For a given location, the relevant climatic parameters of a typical meteorological year are projected on the psychrometric chart (Al-Azri et al., 2013). The most important factor in the development of a bioclimatic chart is the availability of a historical record of weather readings, from which a representative typical year can be developed. Typical meteorological years are usually available for general uses and they are developed using many weather parameters that might not be all of interest in some specific applications. For instance, the typical meteorological year (TMY) developed by Sandia method (Hall et al., 1978) is based on wind speed, dry bulb temperature, wet bulb temperature and solar radiation while bioclimatic charts are mainly dependent on dry bulb temperature and humidity. Most often, what makes typical years hardly available is the scarcity in some weather data required for their development. Solar radiation, for example, is one of the most demanding parameters due to the complexity and cost of measurement methods compared to other parameters. In many such cases, solar measurements are interpolated from nearby locations (Al-Sulaiman and
E-mail address: [email protected].
Ismail, 1997) or are developed from daily or monthly averages instead of using hourly measurements (Al-Rawahi et al., 2013; Habte et al., 2014). This paper discusses the development of a typical year tailored for passive cooling purposes and by using only the relevant parameters which are dry bulb temperature and dew point temperature. Avoiding redundant parameters will not only yield a better representation; it will also make the development of the bioclimatic charts more accessible since dry bulb temperature and wet bulb temperature are readily available from different sources. For example, the National Climatic Data Center (NCDC) of the American National Oceanic and Atmospheric Administration (NOAA) maintains a very comprehensive and freely available archive of worldwide stations.
Typical meteorological year Typical meteorological years (TMY) are very commonly used in energy simulation for quantifying the impact of climatic variables on HVAC and energy systems. The first collection of TMY's was developed for 229 locations in the United States using records gathered between 1948 and 1980. The original method used in constructing TMYs was developed by Sandia National Laboratories (Hall et al., 1978). Different studies showed that TMYs developed by Sandia method give reasonable representation of average climatic behavior (Freeman, 1979). This method was adopted by the National Renewable Energy Laboratory (NREL) in the USA for the development of the subsequent generations of TMYs, known as TMY2 (Marion and Urnab, 1995) and TMY3 (Wilcox and
http://dx.doi.org/10.1016/j.esd.2016.05.001 0973-0826/© 2016 International Energy Initiative. Published by Elsevier Inc. All rights reserved.
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N.A. Al-Azri / Energy for Sustainable Development 33 (2016) 61–74
Marion, 2008), with little modifications and has also been widely adopted worldwide. Sandia method is an empirical approach in which individual months are treated separately and the best representative month will be selected from the historical records, e. g., one January will be selected from all Januarys in the historical records. Then the twelve selected months are concatenated and smoothed at the interface in order to make up the full typical meteorological year. Initially, for each month of the year, five candidate months are selected which are those having the least absolute difference between the short term mean daily cumulative distribution function (CDF) and the long term cumulative distribution function (CDF), which is the average of all mean daily values of all years in the record for that month. This, so-called Finkelstein-Schafer statistics (Finkelstein and Schafer, 1971), difference is given by: FS ¼
Fig. 1. Olgyay's bioclimatic chart (Olgyay, 1963).
1 n ∑ δ n i¼1 i
ð1Þ
where n is the number of days in the month and δ is the absolute difference between the long and short term CDFs of day i. Finkelstein–Schafer statistics (FS) is calculated for a number of parameters used in the selection of the typical month in the TMY. The closeness of the short and long term cumulative distribution functions is based on a weighted sum (WS) of those parameters given by: m
ð2Þ
WS ¼ ∑ j¼1 w j FS j
where w is the weight of parameter j and is selected such that ∑m j = 1 w j = 1. The choice of the parameters and the weights is one of the most common differences in the application of Sandia method. The original weights adopted in Sandia methods were mean solar radiation and the mean, maximum, minimum and range of dry bulb temperature, dew point and wind speed. The same weights were adopted in many applications of the method, e.g. that of Petrakis et al. (1996), but there are also many other works in which these weights were reallocated. Pissimanis et al. (1988), as an example, developed the TMY for Athens, Greece in 1988 in which they assigned no weight to the range parameters and the minimum wind velocity. In their development of TMYs for some sites in Oman, Sawaqed et al. (2005) maintained all parameters of Sandia except the minimum wind velocity and they also replaced the dew point temperature with the relative humidity. Moreover, in the subsequent generations of TMY in the United States, (TMY2/ TMY3) (Marion and Urnab, 1995; Wilcox and Marion, 2008), the direct radiation was added to the list of parameters and all
ranges were removed in addition to the minimum wind velocity. Table 1 shows the different weights in the abovementioned works along with those used in Sandia (Hall et al., 1978). These weights will be used later for the sake of comparison in this paper. Overall, the choice of the parameters and their weights is arbitrarily and is based on the discretion of the developers based on their experience with the targeted use of the typical year. The weighted sum, given in Eq. (2) is calculated for the 12 months in each year in the record. For each month in the year, five candidate years from the record will be selected which correspond to the least weighted sums. The five months will be further screened so that the typical month will be one of the five candidates. In the screening process, firstly, the five candidates are sorted in an ascending order based on their closeness to the long term mean and median of the two parameters: mean ambient temperature and global radiation. Then, the persistence of the mean dry bulb temperature and daily horizontal global radiation is tested. For each candidate month, the number of runs and run lengths are counted for the mean daily dry bulb temperatures that are less than 33rd percentile or greater than 67th percentile of the long term mean and also for the mean daily global radiations that are less than 33rd percentile of the long term mean. Preference in the selection is given to candidates having least number of runs and shortest run lengths. Rigorous details on the development of typical meteorological years can be found in the work of Sawaqed et al. (2005).
Table 1 The different weights used in different works in the development of the TMY. Parameter
Sandia (Hall et al., 1978)
Pissimanis et al. (1988)
TMY2/3 (Marion and Urnab, 1995; Wilcox and Marion, 2008)
Sawaqed et al. (2005)*
Max dry bulb temp Min dry bulb temp Mean dry bulb temp Dry bulb temp range Max dew point temp Min dew point temp Mean dew point temp Dew point temp range Max wind velocity Min wind velocity Mean wind velocity Wind velocity range Global radiation Direct radiation
1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 12/24 0
1/24 1/24 2/24 0 1/24 1/24 2/24 0 2/24 0 2/24 0 12/24 0
1/20 1/20 2/20 0 1/20 1/20 2/20 0 1/20 0 1/20 0 5/20 5/20
1/22 1/22 1/22 1/22 1/22 1/22 1/22 1/22 1/22 0 1/22 1/22 11/22 0
*The relative humidity is used in place of dew point temperature.
N.A. Al-Azri / Energy for Sustainable Development 33 (2016) 61–74
Passive cooling Passive cooling is cooling with minimal mechanical interaction and it has been used in architecture since the times of ancient civilizations. Most passive cooling methods are based on three strategies: mass effect, air movement and evaporative cooling (Szokolay, 2004). High mass In hot climates, where diurnal range in temperature is wide enough (exceeds 6 oC), external walls can be made thick enough to delay the flow of heat into the building by as much as 12 h (Reardon, 2010). High mass with night ventilation is very common in hot dry climates where the diurnal difference can exceed 10 oC. Quantitatively, thermal mass is the density times the specific heat (ρ .Cp) measured in units of (kJ/m3·K) or (Btu/ft3·R). The effectiveness of the high mass is dependent on the thermophysical properties of the material used in the construction of the walls (Reardon, 2010). For an effective heat storage, density (ρ), thermal heat capacity (Cp) and thermal conductivity (k) are chosen such that the thermal diffusivity (k/ρCp) is minimum. Porous materials with low specific heat maintain low thermal mass. Good thermal conductivity and low reflectivity are also required for more effective passive cooling when thermal mass is used (Santamouris and Asimakopoulos, 1996). In addition to the abovementioned thermophysical properties, the effectiveness of the thermal mass also depends on the thickness of the building envelope. When high thermal mass is targeted, the thickness of the wall has to be economically justified through optimal tradeoff between capitalizing on wall thickness and energy saving. Ventilation ASHRAE (2009) defines ventilation air as the air used for providing quality indoor air. In night ventilation, the heat accumulated during the daytime is removed by allowing cold air to enter the building. Cold air replaces hot air naturally using wind and stack effect (thermal buoyancy). This cooling process is based on convective heat transfer from the exposed building envelope (Breesch and Janssens, 2007, 2010). Night ventilation is most effective in moderate to hot climates that have significant diurnal temperature difference and low humidity (Kolokotroni, 1995).
63
Table 2 The weights used in the development of the typical meteorological year for passive cooling. Parameter
Dry bulb temp.
Dew point
Maximum daily value Minimum daily value Mean daily value Daily range
1/10 1/10 2/10 1/10
1/10 1/10 2/10 1/10
Wind provides cooling through air circulation and evaporative cooling. It is applicable in all types of climates but not as effective in tropical climates at high humidity. At moderate temperatures, a wind blowing at a fraction of meter per second can have a cooling effect equivalent to a few temperature degrees when humidity is around 50% (Santamouris and Asimakopoulos, 1996). At higher humidity, wind speed has to be more for the same cooling effect. In regions where there is a reasonable difference between day and night temperatures, night ventilation, carried out naturally or by mechanical means, is used for cooling in order to tolerate the daytime heat gain and also absorb the heat gain of the next day (Shaviv et al., 2001). Evaporative cooling In evaporative cooling, heat is eliminated through the evaporation of water. In direct evaporative cooling, hot air stream is passed over a wet structure where some of the heat is lost to the evaporating water which results in a higher moisture content. In direct evaporative cooling, dry bulb temperature is decreased while relative humidity is increased. In indirect evaporative cooling, dry bulb temperature is decreased without increasing the moisture content. In this method, hot stream is first cooled using direct evaporative method and then it is used to cool the room air in a heat exchanger without direct contact. Evaporative cooling is mostly effective in dry climates where there is enough difference between dry and wet bulb temperatures. Thermal comfort Human thermal comfort is a relative quality which is subject to individual, climatic and cultural differences and hence cannot be defined accurately. For instance, Givoni (1992) mentioned that people living in
Fig. 2. Typical building bioclimatic chart developed by Givoni (1992).
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N.A. Al-Azri / Energy for Sustainable Development 33 (2016) 61–74
Table 3 The 12 locations used in the development of the typical year for passive cooling.
1 2 3 4 5 6 7 8 9 10 11 12
Location
Station name
WMO station ID
Latitude
Longitude
Elevation (m)
Bahrain Cairo, Egypt Christchurch, New Zealand Dakar, Senegal Dubai, UAE Jeddah, Saudi Arabia Kuwait City, Kuwait Muscat, Oman Napoli, Italy New Delhi, India Singapore, Singapore Waco, Texas, USA
Bahrain Int. AP Cairo Int. AP Christchurch Int. AP Leopold Senghor Intl. AP Dubai Int. AP King Abdulaziz Int. AP Kuwait Int. AP Muscat Int. AP Capodichino Safdarjung AP Singapore Changi Int. AP Waco Reg. AP
411500 623660 937800 616410 411940 410240 405820 412560 162890 421820 486980 722560
26.271 30.122 −43.489 14.740 25.255 21.680 29.227 23.595 40.85 28.585 01.350 31.619
50.634 31.405 172.535 −17.490 55.364 39.157 47.969 58.298 14.30 77.206 103.994 −97.228
1.8 116.4 37.5 25.9 10.4 14.6 62.8 8.0 72.0 214.9 6.7 152.4
naturally ventilated, unconditioned buildings usually have more tolerance to changes in the temperature of summer days. MacPherson (1973) defined six factors that can affect the sensation of thermal comfort which are: air speed, mean radiant temperature (MRT), metabolic rate, temperature, humidity and the level of clothing. Fanger (1972) suggested a model that takes into account the human factors, namely the activity level and clothing level, in addition to the climatic variables. His so-called Predicted Mean Vote (PMV) model was developed experimentally from the responses of college-age subjects set in a uniform environment at steady conditions. As its name suggests, this model is developed from the predicted mean of votes raised by a large population of people expressing their thermal sensation in a given environment. Because of the many governing variables affecting comfort, there has not been a definite quantitative expression of a comfort zone. In the
Table 4 The candidate years for Muscat, Oman. Ranked by minimum WS Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
13 5 11 6 12 11 3 1 1 10 9 14
14 10 6 1 14 13 7 10 6 3 14 12
3 14 9 12 6 9 5 13 10 6 11 2
9 9 7 2 9 2 8 12 8 2 4 9
Ranked by persistence criteria 14 10 7 6 4 13 8 13 5 3 14 9
5 3 3 8 4 5 12 11 5 12 12 5
13 14 3 8 12 9 5 12 8 10 9 14
9 3 6 2 6 2 7 11 1 6 11 5
3 5 9 12 14 11 3 1 6 2 4 12
5 9 11 1 9 5 12 10 10 12 12 2
PMV model, comfort is measured qualitatively using a seven-point index scale which is the same as ASHRAE (2009) scale (cold, cool, slightly cool, neutral, slightly warm, warm and hot). However, the quantitative scaling of this method ascends from − 3 to 3 instead of ASHRAE's scale that goes from 1 to 7. In another model developed by MacPherson (1973), a 19-grade index is developed which is a function of one or more of the six factors impacting the level of comfort. In spite of the many variables that decide thermal comfort, there is a range of temperature and humidity values within which the vast majority of people feel satisfied. These ranges are usually used to define the thermal comfort zone in a psychometric chart. Different literature works might slightly vary in outlining the boundaries of a thermal zone, but they all agree on a vast common overlap. The comfort zone set by ASHRAE (2009) allows no consideration to acclimatization to different climates. According to ASHRAE Standard 55, thermal neutrality is defined as the indoor thermal index value for which a mean vote of neutral is expressed on the thermal sensation scale. Earlier works, e.g. Humphreys (1978) expressed thermal neutrality in terms of temperature only and it was correlated to the external air temperature. Comfort was assumed to be achieved at these temperature values provided that other factors such as humidity and clothing are satisfied. Building bioclimatic charts Bioclimatic charts offer a convenient way for predicting, whether or not, passive cooling is likely to improve thermal comfort in a building. The selection of a passive cooling strategy is heavily based on the local climatic conditions. Olgyay's (1963) bioclimatic chart (Fig. 1), developed in the 1950s, was one of the early attempts used for specifying different strategies on a dry bulb temperature versus relative humidity chart. Oglyay's
Table 5 The years used in the construction of the typical meteorological year.
1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12.
Location
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Bahrain Cairo Christchurch Dakar Dubai Jeddah Kuwait Muscat Napoli New Delhi Singapore Waco
2002 2006 2006 2006 2014 2009 2013 2007 2011 2013 2005 2002
2002 2006 2008 2013 2004 2004 2013 2001 2004 2004 2011 2001
2008 2006 2009 2001 2007 2006 2006 2007 2011 2001 2002 2002
2011 2005 2000 2003 2006 2008 2014 2003 2010 2000 2009 2003
2008 2005 2003 2002 2014 2013 2009 2004 2005 2009 2006 2010
2005 2003 2007 2000 2006 2007 2007 2001 2007 2011 2014 2004
2008 2002 2002 2006 2005 2012 2005 2009 2009 2013 2009 2010
2008 2009 2014 2009 2008 2013 2004 2005 2006 2011 2010 2000
2004 2012 2001 2013 2003 2006 2010 2001 2010 2010 2002 2002
2003 2006 2011 2008 2002 2011 2004 2009 2003 2000 2004 2008
2002 2002 2010 2013 2009 2013 2010 2000 2008 2002 2009 2008
2007 2013 2012 2011 2014 2007 2004 2002 2009 2003 2001 2011
N.A. Al-Azri / Energy for Sustainable Development 33 (2016) 61–74
65
Table 6 Typical years for each month in Muscat, Oman, using different development approaches. TMY approach
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Sandia (Hall et al., 1978) Pissimanis (Pissimanis et al., 1988) TMY2/3 (Marion and Urnab, 1995; Wilcox and Marion, 2008) Sawadeq (Sawaqed et al., 2005) This work
2009 2009 2005 2009 2007
2012 2002 2002 2012 2001
2007 2007 2011 2007 2007
2014 2014 2007 2014 2003
2013 2013 2013 2013 2004
2012 2012 2012 2012 2001
2010 2010 2010 2010 2009
2009 2009 2009 2009 2005
2005 2005 2001 2005 2001
2002 2002 2006 2005 2009
2012 2012 2012 2012 2000
2007 2007 2007 2007 2002
chart outlines the comfort zone between 20 and 30 °C. Thermal comfort in indoor spaces is assumed with indoor level of clothing and it is shown at the center of the chart. The chart also shows the effect of other climatic factors such as mean radiant temperature, wind speed and solar radiation on thermal comfort. Slightly above the lower boundary of the zone, shading is necessary for maintaining reasonable level of
comfort. Up to 10 °C below the comfort zone, comfort can be retained provided that there is enough solar radiation in order to offset the decrease in temperature. Similarly, in order to retain comfort within 10 °C above the comfort zone, wind speed can also offset the increase in temperature. Evaporative cooling, as suggested by Olgay, can retain comfort at high temperature values but low humidity.
Fig. 3. (a) The monthly mean dry bulb temperature for the different years in the record and the typical meteorological year. (b) The monthly mean dry bulb temperature for the different years in the record and the typical meteorological year.
66
N.A. Al-Azri / Energy for Sustainable Development 33 (2016) 61–74
Fig. 3 (continued).
Since Olgyay's chart only considers outdoor conditions, it is only applicable for hot humid climates where there is minimal fluctuations between the indoor and the outdoor temperatures which limit its application for low-mass ventilated buildings (Sayigh and Marafia, 1998). A more popular bioclimatic chart is that of Givoni (1992, 1994). The development of Givoni's chart was based on experimental research on buildings in Europe and USA. These buildings were characterized by their low heat gain and thereby the reliability of the charts for high internal gain is questionable (Givoni, 1994; Lomas et al., 2004). Hence, it is mostly applicable for residential and office buildings where heat gain is minimal. Givoni's chart is based on the linear relationship between the temperature amplitude and vapor pressure of the outdoor air (Sayigh
and Marafia, 1998). It identifies the suitable passive cooling technique based on the outdoor climatic condition assuming minimal internal heat gain. Five zones are identified on Givoni's chart which are: thermal comfort, natural ventilation, high mass, high mass with night ventilation and evaporative cooling. Givoni's chart (Fig. 2) is laid on the psychrometric chart and preserves the original psychrometric parameters which are used in the selection of passive cooling strategies. Modifications to Givoni's chart that suit non-domestic buildings can also be found in the literature (Lomas et al., 2004). The natural ventilation zone on Givoni's chart assumes that the indoor mean radiant temperature and the vapor pressure are similar to those at outdoor conditions; which is also an assumption that limits the application of the chart to buildings having medium to high thermal mass (Watson, 1981).
N.A. Al-Azri / Energy for Sustainable Development 33 (2016) 61–74
On the same chart, at high temperatures, mechanical air-conditioning is necessary to keep a habitable environment while on the left of the comfort zone (CZ), heating is needed for restoring comfort using either solar or mechanical heating. The high thermal mass effect is provided using heavy construction that absorbs heat to be reversed in direction overnight (Szokolay, 2004). In hot, dry climates, night ventilation is used in releasing heat through windows, assisted by fans if necessary. Due to its simplicity and preservation of all psychrometric parameters, Givoni's charts are an attractive means for quantifying the applicability of passive cooling strategies. Construction of bioclimatic charts can be found in more rigorous details in the literature (Al-Azri et al., 2013).
67
Methodology In the determination of passive cooling methods as in the abovementioned bioclimatic chart, the main parameters are the drybulb temperature and the relative humidity which can also be evaluated using dry-bulb temperature and dew point. Hence dry bulb temperature and dew point are used in the development of a representative typical year for developing bioclimatic charts. Tailoring the representation to bioclimatic charts, not only will allow better representation of the typical year for this purpose, it will also be more accessible since records of hourly dew point and dry bulb temperatures are more common than other parameters.
Fig. 4. (a) The monthly mean dew point temperature for the different years in the record and the typical meteorological year. The monthly mean dew point temperature for the different years in the record and the typical meteorological year.
68
N.A. Al-Azri / Energy for Sustainable Development 33 (2016) 61–74
Fig. 4 (continued).
The development of the typical year will follow Sandia procedure (Hall et al., 1978) but by only considering dry bulb temperature and dew point. The short term cumulative distribution functions are constructed for the mean values of maximum, minimum, mean and range of daily readings for both dry bulb and dew point temperatures. The long term CDF will be the average of all of these parameters over the years. Finkelstein–Schafer statistics will be calculated using the weights shown in Table 2 of the four parameters. After selecting five candidates for each month, they will be ranked based on their closeness to the long term mean and median using the mean values of the two variables as mentioned in the description of Sandia method. When evaluating the persistence of mean values using
the frequency and run length; for both mean daily dry bulb temperature and mean daily dew point temperature, values below 33rd percentile and above 67th percentile of the long term mean values will be considered. Then the same run length and number of runs criteria are used in the ultimate selection of each month.
Meteorological data The development of the typical year was carried out for 12 locations chosen from different countries around the world. The geographical locations and weather station IDs are shown in Table 3.
N.A. Al-Azri / Energy for Sustainable Development 33 (2016) 61–74
The typical years using the two essential parameters, dry bulb and dew point, were developed for the 12 locations. The variation with the regular typical meteorological year in the application of passive cooling techniques was tested graphically and numerically based on the zones outlined by Givoni (1992). Typical meteorological years for all the locations were taken from Meteonorm 7.0 database developed using solar and meteorological ground measurements. The hourly weather data records for all locations except Muscat, Oman, was extracted from the National Climatic Data Center (NCDC) of the American National Oceanic and Atmospheric Administration (NOAA) which maintains world record in a well-maintained database. The data was claimed online from the database and it exits in a somewhat crude format as received from the original source and hence required further processing to be valid as input to the development of the typical year. The data is provided in html files with 12 files per
69
year and it includes the measurement as provided by the station. Hence, there are many gaps and in many situations readings were taken at fractions of an hour or every few hours rather than every 60 min. A computer code was developed to read the files and reorganize them in a handier format. The data, which originally exists at Greenwich Mean Time, were extracted for 15 years, from 2000 to 2014 and shifted to the local time. Each year was finally made up of 8760 h. The readings of the 29th of February in leap years were ignored. In the handling of the original data, linear interpolation was used to fill small gaps up to 5 h. Gaps that are longer than 5 h were filled by linear interpolation using the existing data of closest days and matched to the same hours of the day. Closer days to the missing points were given higher weights than further days. When in the rare cases, the gap extends to more than a month; interpolation is performed with the neighboring years using existing data at the
Fig. 5. (a) The bioclimatic charts developed using normal TMY (solid lines) and the TMY developed for passive cooling (dashed lines). (b) The bioclimatic charts developed using normal TMY (solid lines) and the TMY developed for passive cooling (dashed lines).
70
N.A. Al-Azri / Energy for Sustainable Development 33 (2016) 61–74
Fig. 5 (continued).
same period of the year. After interpolation, points at the interface of the interpolated period were smoothed if the difference with the neighboring measured reading is greater than 3°C for both dry bulb temperature and dew point temperature. Smoothing was performed using linear regression with two points at each side of the interface. This routine for dealing with the missing data has shown very good consistency with the daily and seasonal fluctuations. Except for Bahrain and Saudi Arabia whose missing points are around 8%, missing points at all other stations are less than 5% with seven stations being around 1% or less. Discussion The modified Sandia method was implemented with the weights given in Table 2. First, five candidates were selected based on the
difference between the short and long term cumulative distribution functions. The five candidates were then ranked based on the absolute differences between short and long term means and medians for both dry bulb and dew point temperatures. The candidate having widest difference will have lesser chance to be selected. In the final screening, the persistence of mean values is evaluated by testing by calculating the frequency and run length above the 67th long term percentile and below the 33rd long term percentile. The number of runs and their lengths are calculated and then in a selective procedure, preference is given to candidates having less number of runs and shortest run length. The different screening is used in order to ensure that while maintaining minimum difference between short and long term CDFs, preference is given to the set that has good distribution around the mean and median.
N.A. Al-Azri / Energy for Sustainable Development 33 (2016) 61–74
71
the persistence criteria. The underlined years are those having the finally selected typical candidate after performing the run counts and length criteria.
Table 4 shows an example of the selection criteria for Muscat, Oman from the candidate years (1:15) corresponding to (2000: 2014) using minimum weighted sum (WS) of Finkelstein–Schafer statistics and
Table 7.a The percentage of monthly hours that fall on each passive cooling zone based on normal TMY (A) and the developed TMY for passive cooling (B).
Cairo, Egypt M
CZ
NV
Christchurch, New Zealand
HM
HMV
EC
CZ
NV
HM
HMV
EC
1
8
9
8
9
8
10
8
10
8
10
23
22
25
23
24
25
24
25
24
26
2
12
14
13
14
13
15
13
15
13
16
15
16
17
17
16
18
16
18
16
18
3
32
29
36
31
37
38
37
38
37
40
11
13
11
13
11
13
11
13
11
13
4
49
38
60
47
61
54
61
54
61
55
3
4
3
4
3
4
3
4
3
4
5
45
48
78
70
83
75
85
78
76
77
1
4
1
4
1
4
1
4
1
4
6
35
21
83
76
81
75
86
83
66
53
0
0
0
0
0
0
0
0
0
0
7
9
0
76
66
40
13
48
32
17
3
0
0
0
0
0
0
0
0
0
0
8
5
1
77
77
28
32
34
41
7
5
0
1
0
1
0
1
0
1
0
1
9
29
19
90
90
72
68
73
70
47
46
1
2
1
2
1
2
1
2
1
2
10
47
48
89
89
74
83
74
85
64
75
3
1
3
1
3
1
3
1
3
1
11
42
49
46
53
45
54
45
54
45
54
7
13
7
14
7
14
7
14
7
14
12
15
16
15
19
15
19
15
19
15
19
19
12
19
13
19
14
19
14
19
14
A
B
A
B
A
B
A
B
A
B
A
B
A
B
A
B
A
B
A
B
Dakar, Senegal M
CZ
NV
Dubai, UAE
HM
HMV
EC
CZ
NV
HM
HMV
EC
1
61
62
68
70
64
63
64
63
63
63
41
50
44
50
43
51
43
51
42
51
2
44
55
59
71
51
61
51
61
49
64
50
61
59
65
58
66
58
66
58
67
3
43
25
69
64
49
34
49
34
47
32
56
60
78
78
78
85
79
85
73
84
4
22
21
70
87
24
28
24
28
23
25
41
27
83
80
86
77
92
82
76
72
5
15
6
93
19
10
19
10
17
9
7
1
53
53
58
45
87
63
35
27
6
2
0
91 100
4
2
4
2
3
1
0
0
44
21
10
18
21
47
2
15
7
1
0
96
99 100
5
0
5
0
1
0
0
0
18
11
4
3
11
16
1
1
8
0
0
92
95
0
0
0
0
0
0
0
0
17
5
3
3
16
24
0
2
9
0
0
86
88
0
0
0
0
0
0
0
0
43
21
5
7
11
21
0
4
10
0
0
90
94
0
4
0
4
0
2
4
1
71
64
36
21
47
29
13
12
11
9
9
99
99
27
30
27
30
16
15
52
33
94
91
93
75
93
76
79
61
12
52
44
95
89
74
69
74
69
66
72
57
73
70
82
68
83
68
83
66
84
A
B
A
B
A
B
A
B
A
B
A
B
A
B
A
B
A
B
A
B
Jeddah, Saudi Arabia M
CZ
NV
Kuwait City, Kuwait
HM
HMV
EC
CZ
NV
HM
HMV
EC
1
57
56
80
89
77
82
77
82
74
76
6
10
6
10
6
10
6
10
6
10
2
49
39
83
78
76
56
76
57
66
50
17
17
17
17
20
20
20
20
21
28
3
35
35
84
84
77
66
79
69
54
54
21
22
24
25
42
39
42
39
52
55
4
23
9
82
78
58
33
63
42
42
21
37
27
52
41
79
70
83
78
87
83
5
8
4
63
63
33
26
47
32
12
9
7
11
23
30
59
62
95
79
84
89
6
2
2
56
54
14
17
25
34
5
6
1
0
8
6
40
25
83
56
59
51
7
0
0
45
34
2
7
17
16
0
0
0
0
5
2
27
21
71
63
43
47
8
0
0
34
18
0
6
1
14
0
3
0
1
11
8
29
31
74
72
40
51
9
0
0
45
34
0
0
0
1
0
0
6
2
26
15
55
32
92
63
81
55
10
1
1
66
64
4
4
5
8
1
2
41
21
66
44
84
55
99
86
97
71
11
11
15
91
89
36
50
37
51
18
33
32
36
42
45
46
53
46
53
45
56
12
42
49
90
90
79
84
79
85
62
76
15
2
16
2
15
2
15
2
15
2
A
B
A
B
A
B
A
B
A
B
A
B
A
B
A
B
A
B
A
B
72
N.A. Al-Azri / Energy for Sustainable Development 33 (2016) 61–74
Table 5 shows the years in the record whose corresponding month has the best representative readings for that specific location based on the weights assigned in this work. The successfully selected 12 were
concatenated to construct the typical year. The data was smoothed at the concatenation interface by using linear regression with 6 h at each side of the interface.
Table 7.b The percentage of monthly hours that fall on each passive cooling zone based on normal TMY (A) and the developed TMY for passive cooling (B).
Manamah, Bahrain M
CZ
NV
Muscat, Oman
HM
HMV
EC
CZ
NV
HM
HMV
EC
1
17
19
17
32
17
19
17
19
17
19
55
59
58
67
56
61
56
61
56
61
2
29
28
30
33
29
28
29
28
29
28
55
59
69
85
66
68
66
68
65
65
3
60
59
67
68
65
71
65
71
64
70
53
43
90
86
84
65
85
65
77
61
4
48
54
95
92
80
89
81
89
69
79
28
14
75
67
78
55
85
65
68
49
5
4
0
63
63
48
51
62
61
18
17
2
1
34
34
43
35
81
65
29
36
6
0
0
40
33
19
12
40
33
3
2
0
0
28
4
11
4
28
34
1
6
7
0
0
16
4
3
3
9
20
0
0
0
0
31
8
0
3
1
12
0
1
8
0
0
5
1
0
0
0
5
0
0
0
0
48
26
1
0
1
3
0
0
9
0
0
38
19
1
3
1
8
0
0
0
0
66
61
2
6
2
11
0
2
10
1
0
73
67
10
12
11
16
3
3
10
8
77
73
53
27
63
31
22
18
11
53
50
92
92
68
66
68
66
63
58
41
30
95
96
74
58
74
58
59
46
12
38
46
42
55
40
47
40
47
39
47
55
39
76
86
65
58
65
58
62
50
A
B
A
B
A
B
A
B
A
B
A
B
A
B
A
B
A
B
A
B
Napoli, Italy M
CZ
NV
New Delhi, India
HM
HMV
EC
CZ
NV
HM
HMV
EC
1
0
0
0
0
0
0
0
0
0
0
8
8
8
8
8
8
8
8
8
8
2
0
0
0
0
0
0
0
0
0
0
28
28
31
32
31
33
31
33
31
32
3
1
1
1
1
1
1
1
1
1
1
39
42
65
61
66
65
67
65
61
63
4
8
9
8
9
8
9
8
9
8
9
34
24
65
59
80
59
97
83
78
51
5
38
43
47
48
42
46
42
46
41
45
11
6
51
51
41
38
64
58
29
29
6
38
34
75
73
52
52
52
52
45
41
1
0
47
38
13
0
18
3
2
0
7
27
32
91
95
64
54
64
55
42
44
0
0
37
27
1
0
2
0
0
0
8
26
37
92
89
55
67
55
69
38
52
0
0
45
45
0
0
0
0
0
0
9
40
50
58
68
48
57
48
57
45
54
0
0
78
74
1
0
1
0
0
0
10
28
22
31
26
29
23
29
23
29
23
24
14
78
76
52
30
53
32
39
22
11
6
11
6
12
6
11
6
11
6
11
37
30
45
47
44
42
44
42
43
39
12
0
0
0
0
0
0
0
0
0
0
17
18
17
18
17
18
17
18
17
18
A
B
A
B
A
B
A
B
A
B
A
B
A
B
A
B
A
B
A
B
Singapore, Singapore M
CZ
NV
Waco, Texas, USA
HM
HMV
EC
CZ
NV
HM
HMV
EC
1
0
0
98
99
0
0
0
0
0
0
8
8
8
10
9
8
9
8
9
8
2
0
0
92
95
0
1
0
1
0
0
10
8
11
13
11
8
11
8
11
8
3
0
0
83
83
0
0
0
0
0
0
17
10
24
21
21
12
21
12
21
11
4
0
0
81
79
0
0
0
0
0
0
30
29
46
52
38
42
38
42
36
39
5
0
0
67
76
0
0
0
0
0
0
17
17
71
73
42
39
42
40
26
23
6
0
0
75
73
0
0
0
0
0
0
12
0
83
87
35
8
36
12
20
0
7
0
0
82
87
0
0
0
0
0
0
9
0
70
74
27
1
31
5
19
0
8
0
0
80
87
0
0
0
0
0
0
10
5
67
61
29
23
37
53
18
8
9
0
0
85
88
0
0
0
0
0
0
23
15
73
76
50
41
52
48
38
24
10
0
0
79
87
0
0
0
0
0
0
23
26
48
45
35
41
35
41
33
40
11
0
0
89
91
0
0
0
0
0
0
17
22
19
24
19
24
19
24
19
26
12
0
0
92
95
0
0
0
0
0
0
7
1
7
3
8
1
8
1
8
1
A
B
A
B
A
B
A
B
A
B
A
B
A
B
A
B
A
B
A
B
N.A. Al-Azri / Energy for Sustainable Development 33 (2016) 61–74
Table 6 shows the typical years for each month using the weights discussed in Table 1. It is apparent that when all parameters in Table 1 are considered, there is little differences amongst the different approaches used. However, when only dry bulb temperature and dew point are considered, the selection is very different. The effect of this difference becomes clearer when the developed TMY is compared to the regular full TMY (e.g. TMY3 approach) with the same historical record of readings. Figs. 3 and 4 show the behaviors of the monthly mean dry bulb temperature and dew point temperature for all years in the record and the constructed typical year for the 12 locations. The mean dry bulb temperature and mean dew point weighs 20% each in the selection of the typical month. Most of the selected typical months have an average that is very close to the average of the mean value on the graph. Other deviations can be explained as a result of the influence of other parameters considered in the weights. Using the developed typical years for dry bulb temperature and dew points, other psychrometric parameters were developed using the equations of thermodynamics for air–water vapor mixture (Al-Azri et al., 2013). Bioclimatic charts were developed for the 12 locations using both the typical meteorological year acquired from Meteonorm 7.0 database and the typical year based on dry bulb temperature and dew point, developed in this work. The applicability of a given passive cooling technique in a given month is usually represented by a line that extends from the pair of average daily minimum dry bulb temperature and average maximum daily humidity to the opposite pair i.e. average daily maximum dry bulb temperature and average minimum daily humidity. The portion of that line segment that falls on a given passive cooling zone indicates the extent to which the passive cooling technique is applicable. Fig. 5a and b shows the bioclimatic charts for the twelve locations. The solid line segments represent each month based on the regular typical meteorological years acquired from Meteonorm 7.0 while the dashed lines are developed from the typical year developed in this work from the historical record of each station. The distribution of both sets of line segments for all locations shows almost similar trends. Moreover, most of the pairs of line segments for a given month lie close to each other. The similarity in the shape of the distribution is expected since dry bulb temperature and dew point usually carry more than 40% of the weight in the selection of the typical year when other parameters such as radiation and wind are also considered.
Fig. 6. Givoni bioclimatic chart for Muscat, Oman using TMY3 approach (Wilcox and Marion, 2008) (solid lines) and the approach developed for passive cooling (dashed lines).
73
Table 8 The percentage of monthly hours that fall into each passive cooling zone in Muscat, Oman based on normal different TMY approaches (A–D) and the developed TMY for passive cooling. Month CZ
NV
HM
HMV
EC
A B C D E A B C D E A B C D E A B C D E A B C D E
1
2
3
4
5
6
7
8
9
10
11
12
31 31 43 31 43 32 32 46 32 45 33 33 47 33 47 33 33 47 33 47 33 33 48 33 48
37 37 38 37 40 45 45 42 45 44 48 48 50 48 49 48 48 50 48 49 49 49 51 49 50
48 48 47 48 47 67 67 65 67 67 80 80 79 80 79 82 82 81 82 81 82 82 83 82 81
21 21 20 21 18 52 52 46 52 47 76 76 75 76 69 98 98 98 98 93 89 89 96 89 79
9 9 9 9 10 47 47 47 47 38 65 65 65 65 68 94 94 94 94 97 69 69 69 69 81
8 8 8 8 8 37 37 37 37 46 53 53 53 53 52 93 93 93 93 90 55 55 55 55 45
0 0 0 0 1 34 34 34 34 32 17 17 17 17 42 41 41 41 41 73 4 4 4 4 21
0 0 0 0 1 48 48 48 48 46 31 31 31 31 48 54 54 54 54 78 19 19 19 19 32
11 11 20 11 21 56 56 58 56 58 63 63 63 63 63 98 98 96 98 97 53 53 59 53 60
47 47 44 49 44 58 58 66 66 60 78 78 74 83 76 99 96 95 96 96 94 94 78 86 87
45 45 45 45 46 77 77 77 77 78 80 80 80 80 81 80 80 80 80 81 77 77 77 77 75
39 39 39 39 37 49 49 49 49 45 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48
A: Sandia (Hall et al., 1978), B: Pissimanis (Pissimanis et al., 1988), C: TMY2/3 (Marion and Urnab, 1995; Wilcox and Marion, 2008), D: Sawaqed (Sawaqed et al., 2005) E: This work.
However, in many situations variation still exists when each passive technique is considered separately for each month. A more quantifiable comparison is given in Tables 7.a and 7.b. The figures in the table represent the percentage of hours per month falling on the comfort zone (CZ), night ventilation (NV), high mass (HM), high mass with night ventilation (HMV) and evaporative cooling (EC). For each zone, there are two sets, (A) those of the TMY extracted from Meteonorm 7.0 database and (B) from the typical year developed in this work. About 50% of the total pairs maintain a relative difference between the two sets that is 12% or less. On the other hand, 25% of the pairs exhibit a relative difference of 35% or more. There has not been a common trend of variation with respect to graphical location, passive technique or time of the year. However, this amount of variation is expected for the essential reason that set B was developed based on dry bulb temperature and dew point only and is supposed to be a better representative for determining passive cooling techniques based on the bioclimatic chart. Other significant reasons for the variation are the subtle differences between the variant choices made when TMY is built even when the same parameters are considered and also another reason is the fact that the two typical years are developed using two different sets of the historical record. Fig. 6 shows the bioclimatic chart for Muscat, Oman using two approaches in developing the TMY and both are based on the same historical record. The solid lines are based on the approach developed by Wilcox and Marion (2008) in developing TMY3 and it is almost identical to other approaches that considers all parameters as shown in Table 6. The dashed lines are based on the typical year developed based on dry bulb temperature and dew point only. The shape of distribution of the two sets of lines is similar but there are still some variations seen between the pairs of each month. This variation is quantified in Table 8 which shows the percentage of readings that fall within the boundaries of each zone using the regular approaches (A–D) and the approach adopted in this paper (E). In most cases, the differences are insignificant to consider except for a few cases.
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N.A. Al-Azri / Energy for Sustainable Development 33 (2016) 61–74
Conclusion Typical meteorological year was developed based on dry bulb temperature and dew point for the purpose of determining the applicability of passive cooling techniques based on bioclimatic charts. Typical meteorological years are usually developed for common purposes and are better when tailored for the purpose of use. Limiting the development of the typical year to dry bulb temperature and dew point should give more accurate representation. When compared to regular TMY, the developed one exhibits similar distribution shape on the chart but it exhibits some variations when compared more specifically by month and passive technique. Acknowledgement The author would like to acknowledge the support provided by the Research Council of Oman (TRC) under Grant Number ORG/EI/12/005. References Al-Azri N, Zurigat Y, Al-Rawahi N. Development of bioclimatic chart for passive building design. Int J Sustain Energy 2013;32(6):713–23. Al-Rawahi N, Zurigat Y, Al-Azri N. Prediction of hourly solar radiation on horizontal and inclined surfaces for Muscat/Oman. J Eng Res 2013;8(2):19–31. Al-Sulaiman F, Ismail B. Estimation of monthly average daily and hourly solar radiation impinging on a sloped surface using the isotropic sky model for Dhahran. Saudi Arab Renew Energy 1997;11(52):257–62. ASHRAE. ASHRAE handbook fundamentals. Atlanta, GA: ASHRAE; 2009. Breesch H, Janssens A. Reliable design of natural night ventilation using building simulation. Proceedings of the thermal performance of the exterior envelopes of whole buildings X international conference; 2007. p. 1–14. ASHRAE; Clearwater Beach, Florida. Breesch H, Janssens A. Performance evaluation of passive cooling in office buildings based on uncertainty and sensitivity analysis. Sol Energy 2010;84:1453–67. Fanger PO. Thermal comfort: analysis and applications in environmental engineering. New York: McGraw-Hill; 1972. Finkelstein J, Schafer R. Improved goodness-of-fit tests. Biometrika 1971;58(3):641–5. Freeman T. Evaluation of the typical meteorological years for solar heating and solar cooling system studies. SERI/TR-8150-1 Golden, CO: Solar Energy Research Institute; 1979.
Givoni B. 1992. Comfort, climate analysis and building design guidelines. Energy Build 1992;1:11–23. Givoni B. Passive low energy cooling of buildings. New York: Van Nostrand Reinhold; 1994. Habte A, Lopez A, Sengupta M, Wilcox S. Temporal and spatial comparison of gridded TMY, TDY, and TGY data sets. National Renewable Energy Laboratory; 2014. Hall I, Prairie R, Anderson H, Bose E. Generation of typical meteorological years for 26 SOLMET stations. SAND78–1601 Albuquerque, NM: Sandia National Laboratories; 1978. Humphreys MA. Outdoor temperature and comfort indoor. Build Res Pract 1978;6: 92–105. Kolokotroni M. Night ventilation in commercial buildings. Annex 28: review of low energy cooling technologies subtask 1. Ottawa: International Energy Agency; 1995. p. 7–11. Lomas KJ, Fiala D, Cook MJ, Copper PC. Building bioclimatic charts for non-domestic buildings and passive downdraught evaporative cooling. Build Environ 2004;39:661–76. MacPherson RK. Thermal stress and thermal comfort. Ergonomics 1973;16(5): 1366–5847. Marion W, Urnab K. User's manual for TMY2s typical meteorological years. NREL US Department of Energy; 1995. Olgyay V. Design with climate, bioclimatic approach and architectural regonalism. New Jersey: Princeton University Press; 1963. Petrakis M, Lykoudis S, Kassomenos. A software tool for the creation of a typical meteorological year. Environ Softw 1996;11(4):221–7. Pissimanis D, Karras G, Notaridu V, Garva K. The generation of a typical meteorological year for the city of Athens. Sol Energy 1988;40:405–11. Reardon C. Home technical manual. Australia: Department of Climate Change and Energy Efficiency; 2010. Santamouris M, Asimakopoulos D. Passive cooling of buildings. Routledge; 1996. Sawaqed NM, Zurigat YH, Al-Hinai H. A step-by-step application of Sandia method in developing typical meteorological years for different locations. Int J Energy Res 2005; 29(8):723–37. Sayigh A, Marafia AH. Thermal comfort and the development of bioclimatic concept in building design. Renew Sustain Energy Rev 1998;2:3–24. Shaviv E, Yezioro A, Capeluto IG. Thermal mass and night ventilation as passive cooling design strategy. Renew Energy 2001;24:445–52. Szokolay SV. Introduction to architectural science: the basis of sustainable design. Oxford: Architectural Press; 2004. Watson D. Analysis of weather data for determining appropriate climate control strategies in architectural design. In: Haisley R, editor. Proceedings of the international passive and hybrid cooling conference; 1981. The International Solar Energy Society; Miami Beach, Florida. Wilcox S, Marion W. User's manual for TMY3 data sets. NREL, US Department of Energy; 2008.