Advertisement

Arabian Journal of Geosciences

, Volume 7, Issue 9, pp 3657–3674 | Cite as

Thermal comfort and forecast of energy consumption in Northwest Iran

  • Gholamreza Roshan
  • Abdolazim Ghanghermeh
  • José Antonio Orosa
Original Paper

Abstract

Assessing the climatic characteristics and identifying the climatic parameters of a specific region can play a major role in human welfare. Thermal comfort conditions are among the most significant factors of climatic variables in the northwestern regions of Iran due to the considerable spatial and temporal variations and are vital for environmental, energy and economic management. It is therefore necessary to advance our knowledge of the climatic conditions in order to provide an appropriate tool for managing climatic extremes. This requires charting of the range of clusters of the thermal comfort conditions in this region. In this study, the general atmosphere circulation model HADCM3 and the A1 scenario, downscaled by the LARS-WG model, were employed to simulate the climatic conditions in Iran during the period 2011–2040. The data obtained were compared with sampled data from six Iranian climatic stations for the 30-year period (1961–1990). In order to tabulate this comparison, six clusters per climatic station were defined based on intrinsic similarity of data. Results show an increase in the annual average temperature of these six stations by 1.69 °C for the predicted years, projected from the base years 1961–1990. This factor has resulted in an increment of the annual average thermal comfort temperature inside buildings by a magnitude of 0.52 °C in future decades. When the thermal requirements of the studied region were evaluated based on the real temperature difference and the degree of thermal comfort, it becomes clear that apart from cluster 1, the energy required to reach thermal comfort inside buildings will increase in the future. As a result of this temperature increase, an increase of the energy required to reach the thermal comfort is expected. This new methodology is an interesting tool and needs to be seriously considered by engineers and architects in designing buildings of the future.

Keywords

Downscaling Clustering Bioclimatic indices Thermal comfort limit Heat stress 

References

  1. Bazrafshan J, Khalili A, Hoorfar A, Torabi S, Hajjaj N (2009) Assessment and comparison of the function of the LARS model and the ClimGen model in simulating the variables of meteorology in different climatic conditions of Iran. J Res Iran Water Resou 3:44–57 (in Persian)Google Scholar
  2. Barriopedro D, Fischer E, Luterbacher J, Trigo R, Garcia-Herrera R (2011) The hot summer of 2010: redrawing the temperature record map of Europe. Science 332:220–224CrossRefGoogle Scholar
  3. Bassil K, Cole D (2010) Effectiveness of public health interventions in reducing morbidity and mortality during heat episodes: a structured review. Int J Environ Res Public Health 7:991–1001CrossRefGoogle Scholar
  4. Babaeian I, Zahra NajafiNik Z (2006) The introduction and evaluation of the LARS-WG model for modeling the meteorological parameters of Khorasan Province in the period 1961–2003. J Nivar 62:49–65, in PersianGoogle Scholar
  5. Babaeian I, Kwon WT (2005) Climate change assessment over Korea using stochastic daily data. Proceeding of the First Iran–Korea Joint Workshop on Climate Modelling. Nov. 2005. Climate Research Institute, Mashad, IranGoogle Scholar
  6. Bezir NC, Akkurt I, Ozek N (2010) Estimation of horizontal solar radiation in Isparta (Turkey). Energy Source A 32:512–517CrossRefGoogle Scholar
  7. Coutts A, Beringer J, Tapper N (2007) Impact of increasing urban density on local climate: spatial and temporal variations in the surface energy balance in Melbourne. Australia J Appl Meteor Climatol 46:477–493CrossRefGoogle Scholar
  8. Cusack L, de Crespigny C, Athanasos P (2011) Heat waves and their impact on people with alcohol، drug and mental health conditions: a discussion paper on clinical practice considerations. J Adv Nurs 67:915–922CrossRefGoogle Scholar
  9. Christenson M, Manz H, Gyalistras D (2006) Climate warming impact on degree-days and building energy demand in Switzerland. Energ Conver Manage 47:671–686CrossRefGoogle Scholar
  10. Diaz J, Linares C, Tobias A (2006) A critical comment on heat wave response plans. Eur J Public Health 16. doi: 10.1093/eurpub/ckl228
  11. D’Ippoliti D, Michelozzi P, Marino C, de’Donato F, Menne B, Katsouyanni K, Kirchmayer U, Analitis A, Medina-Ramon M, Paldy A (2010) The impact of heat waves on mortality in 9 European cities: results from the EuroHEAT project. Environ Health 9. doi: 10.1186/1476-069X-9-37
  12. Dianne L, Ebi L, Forsberg B (2011) Heatwave early warning systems and adaptation advice to reduce human health consequences of heatwaves. Int J Environ Res Public Health 8:4623–4648CrossRefGoogle Scholar
  13. Ebi K, Teisberg T, Kalkstein L, Robinson L, Weiher R (2004) Heat watch/warning systems save lives: estimated costs and benefits for Philadelphia 1995–98. Bull Am Meteorol Soc 85:1067–1073CrossRefGoogle Scholar
  14. Hajat S, O’Connor M, Kosatsky T (2010a) Health effects of hot weather: from awareness of risk factors to effective health protection. Lancet 375:856–863CrossRefGoogle Scholar
  15. Ebi K, Kovats R, Menne B (2005) An approach for assessing human health vulnerability and public health interventions to adapt to climate change. Environ Health Perspect 114:1930–1934Google Scholar
  16. Frank T (2005) Climate change impacts on building heating and cooling energy demand in Switzerland. Energ Build 37:1175–1185CrossRefGoogle Scholar
  17. Ghobadian B, Najafi G, Rahimi H, Yusaf T (2009) Future of renewable energies in Iran. Renew Sust Ener Rev 13:689–695CrossRefGoogle Scholar
  18. Haines A, Kovats R, Campbell-Lendrum D, Corvalan C (2006) Climate change and human health: impacts vulnerability and mitigation. Lancet 367:2101–2109CrossRefGoogle Scholar
  19. Hajat S, Sheridan S, Allen M, Pascal M, Laaidi K, Yagouti A, Bickis U, Tobias A, Bourque D, Armstrong Kosatsky T (2010b) Heat-health warning systems: a comparison of the predictive capacity of different approaches to identifying dangerously hot days. Am J Public Health 100:1137–1144CrossRefGoogle Scholar
  20. IPCC (2007) The physical science basis. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (eds) Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, CambridgeGoogle Scholar
  21. Johnson G, Hanson C, Hardegree S, Ballard E (1996) Stochastic weather simulation: overview and analysis of two commonly used models. J Applied Meteorol 35:1878–1896CrossRefGoogle Scholar
  22. Kovats R, Ebi K (2005) Heatwaves and public health in Europe. Eur J Public Health 16:592–599CrossRefGoogle Scholar
  23. Koppe C, Becker P (2007) Comparison of operational heat health warning systems in Europe, working document of the project “Improving Public Health Responses to Extreme Weather/Heat-Waves-EuroHEAT”. WHO Regional Office for Europe, CopenhagenGoogle Scholar
  24. Lawless C, Semenov M (2005) Assessing lead-time for predicting wheat growth using a crop simulation model. Agricul Forest Mete 135:302–313CrossRefGoogle Scholar
  25. Sajjad Khan M, Coulibaly P, Dibike Y (2006) Uncertainty analysis of statistical downscaling methods. J Hydro 319:357–382CrossRefGoogle Scholar
  26. Meehl G, Tebaldi C (2004) More intense more frequent and longer lasting heat waves in the 21st century. Science 305:994–997CrossRefGoogle Scholar
  27. Matthies F, Menne B (2008) Preparedness and response to heat-waves in Europe from evidence to action. Public health response to extreme weather events. WHO Regional Office for Europe, CopenhagenGoogle Scholar
  28. Majid Zahedi B, Sari Sarraf B, JavidJame IR (2007) The analysis of the temporal-spatial variations of the temperature of the northwest of Iran. J Geogr Develop 4:183–198 (in Persian)Google Scholar
  29. Nicholls N, Skinner C, Loughnan M, Tapper N (2008) A simple heat alert system for Melbourne. Aust Int J Biometeorol 52:375–384CrossRefGoogle Scholar
  30. Orosa JA (2012) New methodology to define probability of buildings energy consumption. Energy Edcu Sci Tech 28(2):897–908Google Scholar
  31. Orosa JA, Oliveira AC (2012) A field study on building inertia and its effects on indoor thermal environment. Renew Energ 37(1):89–96CrossRefGoogle Scholar
  32. Orosa J, Oliveira A (2011) A new thermal comfort approach comparing adaptive and PMV models. Renew Energ 36:951–956CrossRefGoogle Scholar
  33. Qian B, Gameda S, Hayhoe H, De Jong R, Bootsma A (2004) Comparison of LARSWG and AAFC-WG stochastic weather generators for diverse Canadian climates. Climate Res 26:175–191CrossRefGoogle Scholar
  34. Rowshan G, Mohammadi H, Nasrabadi T, Hoveidi H, Baghvand A (2007) The role of climate study in analyzing flood forming potential of water basins. Int J Environ Res 3:231–236Google Scholar
  35. Roshan G, Ranjbar F, Orosa J (2010) Simulation of global warming effect on outdoor thermal comfort conditions. Int J Enviro Sci Tech 7:571–580Google Scholar
  36. Roshan G, Grab S (2012) Regional climate change scenarios and their impacts on water requirements for wheat production in Iran. Int J Plant Product 2:239–265Google Scholar
  37. Roshan G, Oji R, Al-Yahyai S (2013) Impact of climate change on the wheat-growing season over Iran Arab. J Geosci. doi: 10.1007/s12517-013-0917-2 Google Scholar
  38. Roshan G, Rousta I, Ramesh M (2009) Studying the effects of urban sprawl of metropolis on tourism—climate index oscillation: a case study of Tehran city. J Geogr Region Plan 12:310–321Google Scholar
  39. Rey G, Fouillet A, Bessemoulin P, Frayssinet P, Dufour A, Jougla E, Hemon D (2009) Heat exposure and socio-economic vulnerability as synergistic factors in heat-wave-related mortality. Eur J Epidemiol 24:495–502CrossRefGoogle Scholar
  40. Shakoor A, Roshan G, Khoshakhlagh F, Hejazizadeh Z (2008) Effects of climate change process on comfort climate in Shiraz station. Iran J Environ Health Sci Eng 5:269–276Google Scholar
  41. Semenov M, Barrow E (1997) Use of a stochastic weather generator in the development of climate change scenarios. Climate Change 35:397–414CrossRefGoogle Scholar
  42. Semenov M, Brooks R, Barrow E, Richardson C (1998) Comparison of the WGEN and LARS-WG stochastic weather generators in diverse climates. Climate Res 10:95–107CrossRefGoogle Scholar
  43. Semenov M, Barrow E (2002) LARS-WG a stochastic weather generator for use in climate impact studies. User’s manual. Version3.0. http://www.rothamsted.ac.uk/mas-models/download/LARS-WG-Manual.pdf
  44. Semenov M (2007) Developing of high-resolution UKCUP02-based climate change scenarios in the UK. Agric Forest Met 144:127–138CrossRefGoogle Scholar
  45. Webster P, Holland G, Curry J, Chang H (2005) Changes in tropical cyclone number, duration, and intensity in a warming environment. Science 309:1844–1846CrossRefGoogle Scholar
  46. Xu Y, Zhang X, Tian Y (2012) Impact of climate change on 24-h design rainfall depth estimation in Qiantang River Basin. East China, Hydro Proc. doi: 10.1002/hyp.9210 Google Scholar

Copyright information

© Saudi Society for Geosciences 2013

Authors and Affiliations

  • Gholamreza Roshan
    • 1
  • Abdolazim Ghanghermeh
    • 1
  • José Antonio Orosa
    • 2
  1. 1.Department of Geography, Faculty of Human ScienceGolestan UniversityGorganIran
  2. 2.Department of Energy and M.P., E.T.S.NyMUniversity of A CoruñaA CoruñaSpain

Personalised recommendations