Suggestion a new base temperature for calculating the amount of energy demand based on thermal comfort indices and temperature – Physiologic

Author

Assistant Professor, Department of Geography, Golestan University, Gorgan, Iran

Abstract

One of the today`s world issues is saving energy that is not renewable and the use of natural energy not only can make the living environment a comfortable atmosphere but it also greatly reduces the energy consumption. Determining the extent of thermal comfort and the perfect base and in other words, the perfect base temperature to calculate the energy demand for cooling and heating can have a crucial role in energy saving, sustainable development and land use planning. In this paper, it was assumed that the base comfort temperature for each area similar to other climatology phenomena are affected by climatology patterns and behaviors of that area by sticking to a global standard temperature cannot justify regional and station conditions. The crucial point in this study is not only determining bioclimatic climatic comfort but also comfort temperature threshold was modified. In other words, the methodology of this work is such that it does not only search to identify base temperature outside the comfort zone, but in this zone base temperature values have been modified according to the regional climate patterns. In this study, a temperature was introduced as the base temperatures that have played roles in interacting and being influenced by other climatic factors affecting thermal comfort. The main goal of this study is to review and modify the proper base temperature for calculating heating and cooling energies. To determine the new temperature thresholds for providing climate comfort conditions 3 thermal comfort indices of Biker, THI and a temperature-physiological index entitled Predicted Mean Vote have been used. Each of these indicators is presented below. But it should be noted that all climatic data used in the study are daily data from 1960 to 2010 that include wind speed, daily temperature, relative humidity and cloudiness. It must be noted that reconstruction of missing data was performed by linear regression and the results were confirmed after validation of reconstructed data. The monitoring data are being random and their homogeneity were investigated by using run-test and drawing histograms. Importantly, since Iran has different climatic diversity, 10 stations representing different climatic conditions were selected and analyzed. The method of this study to determine and review base temperature that was based on the principle that according to each of the heat and physiological-temperature indices, after gaining the output of each of these models, days in comfort zones were determined. Then separately for each bioclimatic index, the temperatures of days in comfort zone were selected. But despite that all these screened thermal temperatures are in the comfort zone, the methodology was not based on this principle that from the range of extracted thermal temperatures, the minimum and maximum temperatures are introduced as base temperature for calculating HDD and CDD respectively. In the next step, for calibrating this temperature range, percentile method was used. In the following, using various bioclimatic indicators, base temperature values were modeled to calculate HDD and CDD. The main weakness of Biker index was in determining the band and the wide width of thermal comfort range that apart from the base temperature values, it has shown a significant difference compared to the existing standards. About the THI index, although the range of comfort zone reduces comparing to CPI but the long distance of comfort range of Tehran and Babolsar from the existing standards have been identified as the weaknesses of this index. Considering PMV index, it is noteworthy that the range of thermal comfort had little sway compared to the CPI. And it had greater overlap with THI method. One of its strengths is modeling of Tehran`s base temperature which is close to the existing standards. But the weaknesses of this modeling is in determining the base temperature for calculating HDD specifically for 80P and Ahvaz , Babolsar, Rasht, Mashhad. As it was seen, any of the bioclimatic indicators had weaknesses and strengths in modeling of base temperature. This study tried to eliminate these weaknesses by combining the results of all three indicators. The results of combined indices showed that not only the range of fluctuations and on the other hand the values of modeled base temperature are consistent with traditional standards of 18 to 22 or 18 to 24, but also the derived values of this modeling have been able to apply the impact of weather condition of each area in this modeling and provide more realistic results.

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References

خلیلی، ع.، 1383، تدوین یک سامانه جدید پهنه بندی اقلیمی از دیدگاه نیازهای گرمایش- سرمایش محیط و اعمال آن برگستره ایران، مجله تحقیقات جغرافیایی، شماره 4، 5-14.
خلیلی، ع.، 1378، تحلیل سه بعدی درجه - روزهای گرمایش و سرمایش در گستره ایران، فصلنامه تحقیقات جغرافیایی، شماره 54، 44-57.
رزمجویان، م.، 1376، آسایش بوسیله از معماری همساز با اقلیم، انتشارات دانشگاه شهید بهشتی، ص 285.
ریاضی، ج.، 1365، اقلیم و آسایش در ساختمان، گزارش مرکز تحقیقات و مسکن و شهرسازی، تهران.
رمضانی، ب.، ابراهیمی، ه.، 1388، شناخت شرایط مطلوب آسایش بیوکلیماتیک انسانی نوار ساحلی غرب مازندران با روش بیکر، فصلنامه پژوهشی جغرافیا، 88، 57-70.
کسمایی، م.، 1378، .اقلیم و معماری.تهران: انتشارات نشر، 230.
طاووسی، ت.، عطایی، ه.، کاظمی، آ.، 1378، اقلیم و معماری مدارس نوساز اصفهان، جغرافیا و توسعه، 11، 105-93.
ساربانقلی، ح.، اصل بناب، ه.، 1392، معماری همساز با اقلیم دهستان منطقه آذربایجان شرقی با تعیین مناسب ترین شاخص RayMan، فصلنامه جغرافیای سرزمین، 38، 53-62.
لشکری، ح.، داوری، ر.، 1383، تحلیل شرایط بیوکلیمایی انسانی استان آذربایجان غربی به روش بیکر، فصلنامه جغرافیایی سرزمین، 3، 50-34.
میرموسوی، س.ح.، شفیعی، ش.، تقی زاده، ز.، 1393، ارزیابی و برآورد درجه روز و شاخص سازگاری دمایی جهت طراحی مسکن همساز با اقلیم؛
مطالعه موردی: ایستگاه سینوپتیک مهرآباد
تهران، فصلنامه اطلاعات جغرافیایی (سپهر)، 21، 89، 93-81.
Beniston, M. and Diaz, H. F., 2004, The 2003 heat wave as an example of summers in a greenhouse climate? Observations and climate model simulations for Basel, Switzerland. Global Planet Change, 44, 38-55.
Becker, F., 1972, Bioclimatische Reizstufen Fur eine Raumbeurteilung Zur Erholung Bd 76, Hannover.
Cvitan, L. and Jurković, R. S., 2015, Secular trends in monthly heating and cooling demands in Croatia, Theoretical and Applied Climatology, DOI 10.1007/s00704-015-1534-7.
Dombayc, Ö., 2009, Degree-days maps of Turkey for various base temperatures, Energy, 34(11),1807–1812
Esmaili1, R. and Fallah Ghalhari, G., 2014, An Assessment of Bioclimatic Conditions for Tourists-  A Case Study of Mashhad, Iran,  Atmospheric and Climate Sciences, 4, 137-146.
Fanger, P. O., 1970, Thermal Comfort. Copenhagen: Danish Technical Press.
Gosling. S. n., Bryce, E. K., Grady Dixon, P., Gabriel, K. M. A., Gosling, E. Y., Hanes, J. M., Hondula, D. M., Liang, L., Lean, P. A., Muthers, S., Nascimento, S. T., Petralli, M., Vanos, J. K., Wanka, E. R. and Mahillon, V., A., 2014, glossary for biometeorology, Int J Biometeorol, 58, 277–308.
Ghanghermeh, A. A., Roshan, Gh. R., Orosa, A. J., Calvo-Rolle, J. L. and Costa, A. M., 2013, New Climatic Indicators for Improving Urban Sprawl: A Case Study of Tehran City, Entropy, 15, 999-1013
Givoni, G., 1992, Comfort, climate analysis and building design guidelines Energy and Buildings, 18, 1, 11-23.
Givoni, B., 1976, Man, climate and Architecture, 2nd Edition, Applied science publishers, London.
Lam, J. C., Tsang, C. L., Yang, L., Li, D. H. W., 2005, Weather data analysis and design implications for different climatic zones in China, Build Environ 40, 277–296.
Gosling, S. N., McGregor, G. R. and Pa´ldy, A., 2007, Climate change and heat-related mortality in six cities. Part1: Model construction and validation, International Journal of Biometeorology, 51, 66-80.
Hajat, S., Kovats, R. S., Atkinson, R. W. and Haines, A., 2002, Impact of hot temperatures on death in London: A time series approach. J. Epidemiol. Community Health. 56, 110-121.
Huth, R., Kysely, J. and Pokorna, L., 2000, AGCM simulation of heatwaves, dry spells, and their relationships to circulation. Climate Change, 46, 213-222.
 Jeong, J. H. and Kim, D. H., 2013, An Outdoor Comfort Index Framework Based on GIS for Supporting Optimal Environment, nternational Journal of Software Engineering and Its Applications, 7(6), 211-220.
Jiang, F., Li, X., Wei, B., Hu, R. and Li, Z., 2009, Observed trends of heating and cooling degree-days in Xinjiang Province, China, Theor Appl Climatol, 97, 349–360.
Höppe, P. R., 1999, The physiological equivalent temperature–a universal index for the biometeorological assessment of the thermal environment, Int J Biometeorol, 43, 71–75.
Matzarakis, A. and Rutz, F., 2007, RayMan: a tool for tourism and applied climatology, Dev. Tourism Climatol, 9,129–138.
Matzarakis, A., Rutz, F. and Mayer, H., 2007, Modelling Radiation fluxesin simple and complex environments – Application of the RayMan model, International Journal of Biometeorology, 51, 323-334.
Mather, J. R. 1974, climatology: Fundamentals and Applications, New York, McGraw-Hill.
Orosa, J., Costa, A., Fernandez, A. and Roshan, Gh., 2014, Effect of climate change on outdoor thermal comfort in humid climates, J Environ Health Sci Eng, 12, 46, 1-9.
Mehrabi, M., Kaabi-Nejadian, A. and  Khalaji Asadi, M., 2011, Providing a Heating Degree Days (HDDs) Atlas across Iran Entire Zones, World renewable energy congress 2011-Sweden, 8-13 may 2011, Linkoping.
Mokhtari, M. and Anvari, M., 2015, A Comparative study of tourism comforting climate in Iran (case study in the Markazi province and southern Kharasan province of Iran) with TCI model in GIS environment,  Journal of Novel Applied Sciences, 4-2, 151-156.
Olgyay, V., 1967, Bioclimatic Orientation Method for Buildings, Int J Biometeor, 11(2), 163-174.
Roshan, Gh. R., Mirkatouli, G., Shakoor, A. and Mohammad-Nejad, V., 2010, Studying Wind Chill Index as a Climatic Index Effective on the Health of Athletes and Tourists Interested in Winter Sports, Asian J Sports Med, 1(2), 108–116,
Roshan, Gh. R. Orosa, J. A and Nasrabadi, T., 2012, Simulation of climate change impact on energy consumption in buildings, case study of Iran, Energy Policy, 49,731-739.
Roshan, GH., Yousefi, R. and Fitchett, J. M., 2015, Long-term trends in tourism climate index scores for 40 stations across Iran: the role of climate change and influence on tourism sustainability, Int J Biometeorol, doi:10.1007/s00484-015-1003-0.
Rahimzadeh, F., Asgari, A. and Fattahi, E., 2009, Variability of extreme temperature and precipitation in Iran during Recent decades, International Journal of Climatology, 29(3), 329 - 343
Taghavi, F., 2010, Linkage between Climate Change and Extreme Events in Iran, Journal of the Earth & Space Physics, 36(2),33-43
Yildiz, Z. and Sosaoglu, B., 2007, Spatial distributions of heating, cooling, and industrial degree-days in Turkey, Theor Appl Climatol, 90, 249–261.
Zhang, X., Aguilar, E., Sensoy, S., Melkonyan, H., Tagiyeva, H., Ahmed, N., Kutaladze, N., Rahimzadeh, F., Taghipou, A., Hantosh, T.H., Albert, P., Semawi, M., Karam Ali, M., Al-Shabibi, M., Al-Oulan, Z., Zatari, T., Khelet, I., Hamoud, S., Sagir, R., Demircan, M., Eken, M., Adiguzel, M., Alexander, L., Peterson, T. and Wallis, T., 2005, Trends in Middle East climate extreme indices from 1950 to 2003, Journal of geophysical research, 110, D22104, doi:10.1029/2005JD006181.
Journal of the Earth and Space Physics
Volume 43, Issue 3
October 2017
Pages 601-621

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