abdolazim ghanghermeh; Gholamreza Roshan; smaeil shahkooeei
Abstract
Extended Abstract
Introduction
One of the practical indices in determining required energy for providing climatic comfort is the degree day index. The total mean deviation of daily temperature of human comfort temperature (threshold temperature) is called degree day temperature that provides many ...
Read More
Extended Abstract
Introduction
One of the practical indices in determining required energy for providing climatic comfort is the degree day index. The total mean deviation of daily temperature of human comfort temperature (threshold temperature) is called degree day temperature that provides many applications in estimating required energy in cooling and heating section. It is notable that various studies around the world have used different temperatures to calculate HDD and CDD considering their climatic and geographical location. In Iran, 18 degrees centigrade is used for HDD and 24 degrees centigrade for CDD calculation, while climatic and geographical diversity of Iran causes new base temperatures to be recommended for HDD and CDD calculations. The present study plans to present a proper base temperature for calculating HDD and CDD with regard to specific characteristics of each city's climate.
Materials and Method
In the present study to determine the new threshold temperatures in order to provide the energy required for climatic comfort conditions, Olgyay diagram is used. Therefore, the average daily temperature and relative humidity data have been used to draw bioclimatic conditions. Since Iran has different climatic diversity, 10 stations that represent different climatic conditions of Iran were selected and analyzed (Figure 1). It should be mentioned that the duration of time series used includes the statistical period of 1950 to 2010 and these data was collected from Iran`s Meteorological Organization. Since hand drawing of each of the events on Olgyay diagram is cumbersome and time consuming considering the wide range of studied data, therefore, Olgyay diagram was digitalized to receive the output for each station quickly and easily. It is also noteworthy that in this study, Olgyay diagram is divided into 12 bioclimatic classes and the frequency of occurrence of each of the bioclimatic classes for each station in Table (1) has been reported. However, the most important section of this study is related to the determination of new base temperatures for calculating HDD and CDD indices of observational stations. Therefore, based on the days in the comfort zone, three regions in the form of percentile thresholds of 40 to 60 were selected as the representative of the central 20 percent of the data, percentile threshold of 25 to 75 percent as the representative of the dominant central 50% of the data, and finally percentile threshold of 10 to 90 as the central 80 % of the data were selected, and these domains were introduced as new thermal comfort for determining the base temperatures for HDD and CDD calculation (equation 1):
Equation 1:
In equation 1, LP is an equivalent for the threshold rank of the percentiles 10, 25, 40, 60, 75 and 90 percent, n is an equivalent for the number of samples and s is an equivalent for percentiles.
In the final step, after determining the base temperature, required cooling day-degree values (Equation 2) and heating (Equation 3) are calculated as follows:
Equation 2:
Equation 3:
In formula (2) and (3), cooling requirement is calculated by CDD and heating requirement is calculated by HDD for a given period of N days. In these formulae, T is the average daily temperature and è is the base temperature that with regard to the threshold of different percentiles, different numbers are proposed for each station.
Findings
Findings of this section showed that Shiraz and Esfahan have experienced the most ideal conditions of comfort with 35.22 and 33.22 percent of frequency of days in the comfort zone respectively and Babolsar with 83.2 percent of frequency has had the lowest percentage of days with thermal comfort. Among the observational stations, the most frequent occurrence experience of frost and freezing belongs to Sanandaj, and for the stations in Makoo, Shiraz, Tehran and Tabas, the most important preventive factor for the occurrence of comfort conditions is frost and freezing. But, Jask and Bushehr have had the most experience of the days with heat stroke risks and this factor is the most important preventive factor for comfort in these two stations. Although extreme dryness is the most important preventive factor for comfort in Ahvaz, but in Rasht and Babolsar, excess moisture is the most important factor of the lack of comfort. The results indicated that Olgyay diagram has perfectly shown the climatic and bioclimatic differences of various regions. For example, for the coastal cities of the Persian Gulf and Oman Sea, the type of data distribution on the diagram showed that climatic and bioclimatic characteristics of the two cities of Bushehr and Jask differ from Ahwaz, so that the dominant climatic regime of Bushehr and Jask due to the high humidity experience, are affected by the water zone of the Persian Gulf and Oman Sea, but Ahwaz is affected both by the water body of the Persian Gulf and hot and dry systems that pass directly through the Saudi Arabia.
Conclusion
Based on the main objective of this research, new thermal comfort thresholds for all study stations were proposed and the results showed that according to various percentiles, minimum base temperature for calculating HDD belonged to Babolsar station and maximum base temperature for calculating CDD belonged to Shiraz station. It is also worth noting that the sensitivity of the proposed method is such, that minimum differences in the domain and base temperature of thermal comfort are visible even for the stations located in a nearly similar geographical area, and this could indicate the validity of the proposed method. Finally, monthly and annual long-term average of HDD and CDD indices were calculated for the studied cities using proposed thresholds and base temperatures. The results of this section showed that in most observational stations, the months of January, December and February have had the maximum HDD requirements and the maximum CDD requirement was calculated for the months of July and August. The research findings reveal that maximum average annual HDD and CDD requirements belong to Makoo and Jask respectively. The results of this study point to the fact that the need for heating energy has been higher than the need for cooling energy for most of the studied cities. Therefore, the findings show that, based on the proposed method, which is derived from the climatic characteristics and experimental data of each station, a more logical thermal comfort thresholds for the studied stations are presented.
Faramarz Khosh Akhlagh; Gholamreza Rowshan; Reza Borna
Volume 17, Issue 67 , October 2008, , Pages 75-80
Abstract
In this study, using 33-year statistics (1970 - 2003) concerning radiation, cloud density and wind parameters, the study of the feasibility of establishing solar power plants in arid regions of Iran has been conducted. Further, considering the station of Yazd as the most suitable geographic location ...
Read More
In this study, using 33-year statistics (1970 - 2003) concerning radiation, cloud density and wind parameters, the study of the feasibility of establishing solar power plants in arid regions of Iran has been conducted. Further, considering the station of Yazd as the most suitable geographic location for the establishment of solar power plant, stations in its adjacent regions such as Isfahan, Kerman, and Zahedan have been compared in terms of radiation regime and other climatic elements effective in the establishment of solar power plant. After identifying the climatic variations of the stations, and next, the use of statistical methods of standard deviation, coefficient of variation, T-test and ..., Isfahan was introduced as the station most similar to that of Yazd for the establishment of solar power plant.
Hossein Mohammadi; Gholam Reza Roshan
Volume 16, Issue 61 , May 2007, , Pages 50-53
Abstract
On Monday, August 29, 2005, the Hurricane Katrina swept the South American coast of the Gulf of Mexico at a speed of 250 kilometers per hour, causing thousands of deaths and enormous financial losses in three states of Louisiana, Alabama and Mississippi. Although Katrina was a natural disaster, it was ...
Read More
On Monday, August 29, 2005, the Hurricane Katrina swept the South American coast of the Gulf of Mexico at a speed of 250 kilometers per hour, causing thousands of deaths and enormous financial losses in three states of Louisiana, Alabama and Mississippi. Although Katrina was a natural disaster, it was nevertheless man-made, caused by abnormal actions of human beings on natural environments and the increasing use of greenhouse gases in industrialized countries, especially in the United States. In the following, it can be stated that one of the political consequences of Katrina's occurrence has been to disturb security and public order during the occurrence of this incident.
Faramarz Khosh Akhlagh; Gholamreza Roshan
Volume 15, Issue 57 , May 2006, , Pages 42-46
Abstract
In this paper, drought in three stations in Fars province has been investigated based on the three indicators of SIAP, PNPI and RAI. After calculating the coefficients of drought indices for the three stations, considering the growth rate of the indices, the coefficient of variation, correlation and ...
Read More
In this paper, drought in three stations in Fars province has been investigated based on the three indicators of SIAP, PNPI and RAI. After calculating the coefficients of drought indices for the three stations, considering the growth rate of the indices, the coefficient of variation, correlation and trend rate of the indices, each of the indices has been compared among the stations, and each of the indices is evaluated at the stations. The results of the growth rate showed that there is a close relationship between the growth rate of the SIAP and the PNPI indices in Shiraz station and between the PNPI and RAI indices in Abadeh station. Regarding the dispersion coefficient, it can be stated that the least dispersion is in the RAI index in the Abadeh station. But the lowest dispersion in the SIAP and PNPI indicators are in Shiraz Station. Regarding the coefficient of correlation among the indicators and the statistical period, the situation is the same, so that the highest correlation is between the RAI index and the years in question at Abadeh station. However, the highest correlation exists between SIAP and PNPI indices with the years of statistical period in Shiraz station. Finally, the results of the general trend of the indices in the three stations are relatively the same.
