Climate Change Research

Climate Change Research

Comparison of the Impact of Climate Change on Household Energy Consumption Patterns for Two Different Climate Types in the Middle East and Eurasia: A Case Study of Tehran and Moscow

Document Type : Original Article

Authors
zohre ebrahimi 1
Maryam Arab 2
1 Graduate of Climate Risk, Golestan University
2 PhD Student, Department of Architecture, University of Tehran, Tehran, Iran
10.30488/ccr.2022.338551.1074
Abstract
In the present study, in order to monitor and project the impact of global warming on household energy consumption pattern, a comparative study was conducted between the Middle East and Eurasia as two different climates.Tehran as a cold semi-arid climate and Moscow as a humid continental climate have been evaluated. In this study, two-time series of climate data have been used to model the residential energy demand. This paper contained the basic data from 1990 to 2010, and the CMIP5 climate models have been used to project climate data (radiation, temperature, wind speed, and relative humidity) from the outputs of CanEMS2 model, which its values have been dynamically downscaled using the RegCM4.6 for the period from 2020 to 2049. In addition, RCP4.5 scenario was used in this study.The results of this study demonstrated that the mean annual temperature for the period 2020–2049 as compared with the current decade would be increased 3.27°C and 4.71°C for Tehran and Moscow, respectively. On the other hand, relative humidity changes in future compared to base period would be decreased 4% for Tehran and increased 10.5% for Moscow. The total assessment on climate change in the coming decades would be led to a change in energy demand pattern for both study areas. Although heating energy demand is reduced in both study areas, the rate of decrease in Tehran was 12.75 percent, and it was in Moscow one percent, compared to the base period. On the other hand, the rate of increase in cooling energy in Tehran was 12.74 percent, and it was in Moscow One percent, compared to the base period. In general, the percentage of increased cooling energy demand compared to the reduced heating energy for both stations was high, which would be resulted in more emissions of CO2 (carbon dioxide) from these buildings.
Keywords
Climate Scenario
Building Modeling
Energy Consumption Pattern
Carbon Dioxide (CO2) Emissions
Climate Adaptation
  1. Arima, Y., Ooka, R., Kikumoto, H., Yamanaka, T., 2016. Effect of climate change on building cooling loads in Tokyo in thesummers of the 2030s using dynamically downscaled GCM data. Energy and Buildings, 114, 123–129
  2. Borowska, J., Jezierski, W., 2018, Heat parameters of multi-sash windows in residential buildings,  Construction and Building Materials, 176, 112-117
  3. Cao, J., Li, M., Wang, M., Xiong, M., Meng, F., 2017, Effects of climate change on outdoor meteorological parameters for building energy-saving design in the different climate zones of China, Energy and Building, shttp://dx.doi.org/10.1016/j.enbuild.2017.04.045
  4. Crawley, D.B., 2005, Contrasting the Capabilities of Building Energy Performance Simulation Programs. US Department of Energy, Washington. DC.USA.
  5. Dwaikat, L. N., Ali, K. N., 2018, The economic benefits of a green building – Evidence from Malaysia, Journal of Building Engineering, 18, 448-453
  6. Fasi, M.A., Budaiwi, I.M., 2015, Energy performance of windows in office buildings considering daylight integration and visual comfort in hot climates, Energy and Buildings, 108, 307–316.
  7. Henninger, R.H., Witte, M.J., 2003, EnergyPlus Testing with ANSI/ASHRAE Standard 140-2001 (BESTEST). EnergyPlus Version 1.1. 0.020, Ernest Orlando Lawrence Berkeley National Laboratory Berkeley, California, for US Department of Energy, Washington.
  8. Judkoff, R., Neymark, J., 1995, International Energy Agency building energy simulation test (BESTEST) and diagnostic method. National Renewable Energy Lab. Golden, CO (US). No. NREL/TP-472-6231
  9. Kingsborough, A., Jenkins, K., Hall, J. W., 2017, Development and appraisal of long-term adaptation pathways for managing heat-risk in London, Climate Risk Management, 16, 73-92
  10. Kwok, Y.T., Lai, A.K.L., Lau, K.K-l.L., Chan, P.W., Lavafpour, Y., Ho, J.C.K., Yung Ng, E.Y., 2017. Thermal comfort and energy performance of public rental housing under typical and near-extreme weather conditions in Hong Kong. Energy and Buildings,https://doi.org/10.1016/j.enbuild.2017.09.067.
  11. Maile, T., Fischer, M., Bazjanac. V., 2007, Building energy performance simulation tools a life-cycle and interoperable perspective, Center for Integrated Facility Engineering, 1-49.
  12. Martinaitis, V., Zavadskas, E.K., Motuziene, V., Vilutiene, T., 2015, Importance of occupancy information when simulating energy demand of energy efficient house: A case study, Energy and Buildings, 101, 64–75.
  13. Mateus, N.M., Pinto, A., da Graça, G.C., 2014, Validation of EnergyPlus thermal simulation of a double skin naturally and mechanically ventilated test cell, Energy Build, 75, 511-522.
  14. Maltais, L. G.,  Gosselin, L., 2017, Daylighting ‘energy and comfort’ performance in office buildings: Sensitivity analysis, metamodel and pareto front, Journal of Building Engineering ,14, 61-72
  15. Meniru, K., Rivard, H., Bédard, C., 2003, Specifications for computer-aided conceptual building design, Design Studies, 24, 51-71

 

  1. Piot, A., Béjat, T.,Jay, A.,Bessette, L.,Wurtz, E.,Barnes-Davin, L., 2017, Study of a hempcrete wall exposed to outdoor climate: Effects of the coating, Construction and Building Materials,

139,  540-550

  1. Rahman, M.M., Rasul, M.G., Khan, M.M.K., 2010, Energy conservation measures in an institutional building in sub-tropical climate in Australia, Applied Energy,87, 2994–3004.
  2. Sköld, Y. A., Thorsson, S., Rayner, D., Lindberg, F., Janhäll, S., Jonsson, A., Moback, U., Bergman, R., Granberg, M., 2015, An integrated method for assessing climate-related risks and adaptation alternatives in urban areas, Climate Risk Management, 7, 31-50
  3. Shen, P., Malkawi, A. M., 2016, Impacts of climate change on US building energy use by using downscaled hourly future weather data, Energy and Buildings ,http://dx.doi.org/10.1016/j.enbuild.2016.09.028
  4. Shibuya, T., Croxford, B., 2016, The effect of climate change on office building energy consumption in Japan, Energy and Buildings, 117, 1–11
  5. Shrestha, S., Maxwell, G., 2011, Empirical validation of building energy simulation software: EnergyPlus, 2935-2942.
  6. Wise, R.M., Butler,R.A., Suadnya, W., Puspadi,d K.,Suharto, I., Skewes,T.D., 2016, How climate compatible are livelihood adaptation strategies and development programs in rural Indonesia?, Climate Risk Management, 12, 100-114
  7. Yuan, T., Ding, Y., Zhang, Q., Zhu, N., Yang, K., He, Q., 2017, Thermodynamic and economic analysis for ground source heat pump system coupled with borehole free cooling, Energy and Buildings,
  8. Zhai, Z.J., Johnson, M., Krarti, M., 2011, Assessment of natural and hybrid ventilation models in whole-building energy simulations, Energy and Buildings, 43, 2251-2261.
  9. Zhu, M., Pan, Y., Huang, Z., Xu, P., 2016, An alternative method to predict future weather data for buildingenergy demand simulation under global climate change, Energy and Buildings, 113, 74–86