Samareh Abolhassani, Soroush (2018) Climate Change Impacts on Thermal Performance of Residential Buildings. Masters thesis, Concordia University.
Preview |
Text (application/pdf)
3MBSamareh Abolhassani_MASc_F2018 .pdf - Accepted Version Available under License Spectrum Terms of Access. |
Abstract
Climate change has altered regular temperature patterns and various climate variables on a global scale, causing growing concerns about future food, water and energy security. Immediate action should be taken to understand the extent of climate change while also proposing adaptation strategies to cope with the projected future climate conditions. From the energy security perspective, particularly in consideration of the ever-increasing human population, an important aspect to understand is the impact of climate change on the energy consumption of residential buildings. Understanding the impact of climate change on building energy consumption is not only beneficial for advising efficient energy-saving measures, but also for understanding future energy requirements. Various studies have already shown that climate change effects heating and cooling loads of buildings in various climates around the world. However, there is a lack of comprehension of the effects of climate change on energy consumption in Quebec. In addition, some of the methodologies employed to address the impact of climate change in buildings may be not be accurate or accessible to practitioners. The present study tries to fill this gap by advising a simple procedure that can be implemented in day-to-day engineering practice for a detailed understanding of the effects of climate change on building energy consumption. The methodology is applied to a residential building in Montreal, Quebec (Canada), using the state-of-the-art climate model projections for the periods of 2011-2040 (short-term future), 2041-2070 (midterm future), and 2071-2100 (long-term future) and under low and high greenhouse gas concentration scenarios. In brief, the available projections of five global climate models was studied for two particular weather parameters, namely dry-bulb temperature and shortwave radiation. The projections were downscaled at the point location and at an hourly resolution using a cascade model based on a quantile mapping bias correction method and a modified quantile-based k-nearest neighbor method. The downscaled projections were used as inputs to TRNSYS, an energy simulation software, in order to quantify the heating and cooling loads as well as judge the overall performance of the residential building in Montreal. This methodology can provide a basis for detailed understanding of the impacts of climate change on building energy consumption. Considering the applied case study, it is understood that climate change will not only change the intensity of the heating and cooling loads but can also change the empirical distribution of hourly energy consumption, particularly during peak loads.
| Divisions: | Concordia University > Gina Cody School of Engineering and Computer Science > Building, Civil and Environmental Engineering |
|---|---|
| Item Type: | Thesis (Masters) |
| Authors: | Samareh Abolhassani, Soroush |
| Institution: | Concordia University |
| Degree Name: | M.A. Sc. |
| Program: | Building Engineering |
| Date: | 4 July 2018 |
| Thesis Supervisor(s): | Haghighat, Fariborz and Nazemi, Ali |
| Keywords: | climate change, building thermal performance, downscaling, disaggregation, bias correction |
| ID Code: | 984122 |
| Deposited By: | Soroush Samareh Abolhassani |
| Deposited On: | 13 Aug 2018 17:52 |
| Last Modified: | 13 Aug 2018 17:52 |
References:
[1] Beer J, Mende W, Stellmacher R. The role of the sun in climate forcing 2000;19:403–15.[2] Cubasch U, Wuebbles D, Chen D, Facchini MC, Frame D, Mahowald N, et al. Introduction in Climate Change 2013. Intergov Panel Clim Chang 2013 Phys Sci Basis Contrib Work Gr I to Fifth Assess Rep Intergov Panel Clim Chang 2013:119–58. doi:10.1017/CBO9781107415324.007.
[3] NASA. Earth Observatory 2018. https://earthobservatory.nasa.gov/Features/EnergyBalance/page4.php.
[4] Lowe DC, Zealand N. Changes in Atmospheric Constituents and in Radiative Forcing (IPCC 2007). Change 2007;30:129–234. doi:10.1103/PhysRevB.77.220407.
[5] Pachauri, Rajendra K. et al. Climate Change 2014 Synthesis Report Summary Chapter for Policymakers. Ipcc 2014:31. doi:10.1017/CBO9781107415324.
[6] University of East Anglia. 2017 likely to be third warmest year on record 2017. https://phys.org/news/2017-11-warmest-year.html.
[7] Flato, G., Marotzke, J., Abiodun, B., Braconnot, P., Chou, S.C., Collins, W.J., Cox, P., Driouech, F., Emori, S., Eyring, V. and Forest C. Climate Change 2013 The Physical Science Basis 2013;5:741–866.
[8] Boorman, P., McDonald, R. and Hill S. Climate change scenarios for the United Kingdom: the UKCIP02 scientific report. Tyndall Centre for Climate Mental Sciences University 2002;96:218224.
[9] Ierley GR. General circulation models. Earth Syst Environmetal Sci 2001:20–4.
[10] Wilby RL, Troni J, Biot Y, Tedd L, Hewitson BC, Smith DM, et al. A review of climate risk information for adaptation and development planning. Int J Climatol 2009;29:1193–215.
[11] Taylor KE, Stouffer RJ, Meehl GA. An Overview of CMIP5 and the Experiment Design. Bull Am Meteorol Soc 2011;93:485–98. doi:10.1175/BAMS-D-11-00094.1.
[12] Obasi GOP, Töpfer K. Emissions Scenarios: Summary for Policymakers; a Special Report of IPCC Working Group III. Intergovernmental Panel on Climate Change; 2000.
[13] Moss RH, Edmonds JA, Hibbard KA, Manning MR, Rose SK, Vuuren DP Van, et al. The next generation of scenarios for climate change research and assessment. Nature 2010;463:747–56. doi:10.1038/nature08823.
[14] Society A, Law I. The Kyoto Protocol to the United Nations Framework Convention on Climate Change. Am Soc Int Law ; Cambridge Univ Press Stable URL 2017;92:315–31.
[15] Nakicenovic N, Alcamo J, Davis G, de Vries B, Fenhann J, Gaffin S, et al. Special report on emissions scenarios: a special report of Working Group III of the Intergovernmental Panel on Climate Change. 2000.
[16] Jentsch MF, James PAB, Bourikas L, Bahaj AS. Transforming existing weather data for worldwide locations to enable energy and building performance simulation under future climates. Renew Energy 2013;55:514–24. doi:10.1016/j.renene.2012.12.049.
[17] Alley RB, Berntsen T. Summary for Policymakers Summary for Policymakers. A Rep Work Gr I Intergov Panel Clim Chang 2000.
[18] Vuuren DP Van, Edmonds J, Kainuma M, Riahi K, Nakicenovic N, Smith SJ, et al. The representative concentration pathways : an overview. Clim Chang 2011:5–31. doi:10.1007/s10584-011-0148-z.
[19] Belcher SE, Hacker JN, Powell DS. Constructing design weather data for future climates. Build Serv Eng Res Technol 2005;26:49–61.
[20] Jee J, Kim S. Sensitivity Study on High-Resolution WRF Precipitation Forecast for a Heavy Rainfall Event. Atmosphere (Basel) 2017. doi:10.3390/atmos8060096.
[21] Brown C, Greene A, Block P, Giannini A. Review of downscaling methodologies for Africa climate applications. Int Res Inst Clim Soc Columbia Univ 2008;15:5–8.
[22] Trzaska S, Schnarr E. A review of downscaling methods for climate change projections. United States Agency Int Dev by Tetra Tech ARD 2014:1–42.
[23] Goodess CM, Anagnostopoulou C, Bárdossy A, Frei C, Harpham C, Haylock MR, et al. An intercomparison of statistical downscaling methods for Europe and European regions – assessing their performance with respect to extreme temperature and precipitation events 2005 ( published as CRU RP11 in 2012 ) Climatic Research Unit School of Environmental Sciences University of East Anglia About the Climatic Research Unit CRU Research Publications series 2012;2005.
[24] Chen J, Brissette FP, Chaumont D, Braun M. Performance and uncertainty evaluation of empirical downscaling methods in quantifying the climate change impacts on hydrology over two North American river basins. J Hydrol 2013;479:200–14. doi:10.1016/j.jhydrol.2012.11.062.
[25] Sunyer MA, Madsen H, Ang PH. A comparison of different regional climate models and statistical downscaling methods for extreme rainfall estimation under climate change. Atmos Res 2012;103:119–28. doi:10.1016/j.atmosres.2011.06.011.
[26] Zorita E, Von Storch H. The analog method as a simple statistical downscaling technique: comparison with more complicated methods. J Clim 1999;12:2474–89.
[27] Shen P. Impacts of climate change on U . S . building energy use by using downscaled hourly future weather data. Energy Build 2017;134:61–70. doi:10.1016/j.enbuild.2016.09.028.
[28] Wang L, Liu X, Brown H. Prediction of the impacts of climate change on energy consumption for a medium-size office building with two climate models. Energy Build 2017;157:218–26. doi:10.1016/j.enbuild.2017.01.007.
[29] Huang K, Hwang R. Future trends of residential building cooling energy and passive adaptation measures to counteract climate change : The case of Taiwan. Appl Energy 2016;184:1230–40. doi:10.1016/j.apenergy.2015.11.008.
[30] Wang H, Chen Q. Impact of climate change heating and cooling energy use in buildings in the United States. Energy Build 2014;82:428–36. doi:10.1016/j.enbuild.2014.07.034.
[31] Hamlet AF, Salathé EP, Carrasco P. Statistical Downscaling Techniques for Global Climate Model Simulations of Temperature and Precipitation with Application to Water Resources Planning Studies 2010.
[32] Wood AW, Leung LR, Sridhar V, Lettenmaier DP. Hydrologic implications of dynamical and statistical approaches to downscaling climate model output. Clim Chang 2004:189–216.
[33] Livneh, Ben, Eric A. Rosenberg, Chiyu Lin, Bart Nijssen, Vimal Mishra, Kostas M. Andreadis, Edwin P. Maurer and DPL. A long-term hydrologically based dataset of land surface fluxes and states for the conterminous United States: Update and extensions. J Clim 2002;26:9384–92. doi:10.1175/JCLI-D-12-00508.1.
[34] Gudmundsson L, Bremnes JB, Haugen JE. Technical Note : Downscaling RCM precipitation to the station scale using statistical transformations – a comparison of methods. Hydrol Earth Syst Sci 2012:3383–90. doi:10.5194/hess-16-3383-2012.
[35] Piani C, Haerter JO, Coppola E. Statistical bias correction for daily precipitation in regional climate models over Europe. Theor Appl Climatol 2010:187–92. doi:10.1007/s00704-009-0134-9.
[36] Teutschbein C, Seibert J. Bias correction of regional climate model simulations for hydrological climate-change impact studies : Review and evaluation of different methods. J Hydrol 2012;456–457:12–29. doi:10.1016/j.jhydrol.2012.05.052.
[37] Leander R, Buishand et al. Estimated changes in flood quantiles of the river Meuse from resampling of regional climate model output 2008:331–43. doi:10.1016/j.jhydrol.2007.12.020.
[38] Teutschbein C, Seibert J. Is bias correction of regional climate model (RCM) simulations possible for non-stationary conditions ? 2013:5061–77. doi:10.5194/hess-17-5061-2013.
[39] Christensen JH, Boberg F, Christensen OB, Lucas-picher P. On the need for bias correction of regional climate change projections of temperature and precipitation. Geophys Res Lett 2008;35. doi:10.1029/2008GL035694.
[40] Maraun D. Nonstationarities of regional climate model biases in European seasonal mean temperature and precipitation sums. Geophys Res Lett 2012;39:1–5. doi:10.1029/2012GL051210.
[41] Camici S, Brocca L, Melone F, Moramarco T. Impact of Climate Change on Flood Frequency Using Different Climate Models and Downscaling Approaches. J Hydrol Eng 2014;19:4014002. doi:10.1061/(ASCE)HE.1943-5584.0000959.
[42] Piani C, Haerter JO. Two dimensional bias correction of temperature and precipitation copulas in climate models. Geophys Res Lett 2012;39:1–6. doi:10.1029/2012GL053839.
[43] Michelangeli P, Vrac M, Loukos H. Probabilistic downscaling approaches : Application to wind cumulative distribution functions. Geophys Res Lett 2009;36:2–7. doi:10.1029/2009GL038401.
[44] Willems P, Vrac M. Statistical precipitation downscaling for small-scale hydrological impact investigations of climate change. J Hydrol 2011;402:193–205. doi:10.1016/j.jhydrol.2011.02.030.
[45] Sunyer MA, Madsen H, Rosbjerg D, Arnbjerg-Nielsen K. A Bayesian Approach for Uncertainty Quantification of Extreme Precipitation Projections Including Climate Model Interdependency and Nonstationary Bias. J Clim 2014:7113–32. doi:10.1175/JCLI-D-13-00589.1.
[46] G. Bürger, S. R. Sobie, A. J. Cannon, A. T. Werner and TQM. Downscaling Extremes — An Intercomparison of Multiple Statistical Methods for Present Climate. J Clim 2013:4366–88. doi:10.1175/JCLI-D-11-00408.1.
[47] Alex J. Cannon, Stephen R. Sobie and TQM. Bias Correction of GCM Precipitation by Quantile Mapping : How Well Do Methods Preserve Changes in Quantiles and Extremes? J Clim 2015:6938–59. doi:10.1175/JCLI-D-14-00754.1.
[48] Li H, Sheffield J, Wood EF. Bias correction of monthly precipitation and temperature fields from Intergovernmental Panel on Climate Change AR4 models using equidistant quantile matching. Atmospheres 2010;115. doi:10.1029/2009JD012882.
[49] Urg J, Frei C, Vidale PL. Downscaling from GCM precipitation: A benchmark. J Climatol 2006;689:679–89. doi:10.1002/joc.1287.
[50] Bo J, Terray L, Habets F, Martin E. Statistical and dynamical downscaling of the Seine basin climate for hydro-meteorological studies. Int J Climatol 2007;1655:1643–55. doi:10.1002/joc.
[51] Chen J, Brissette P, Chaumont D, Braun M. Finding appropriate bias correction methods in downscaling precipitation for hydrologic impact studies over North America. Water Resour Res 2013;49:4187–205. doi:10.1002/wrcr.20331.
[52] Pierce DW, Das T, Cayan DR, Maurer EP, Snyder MA, Sloan LC, et al. Probabilistic estimates of future changes in California temperature and precipitation using statistical and dynamical downscaling. Clim Dyn 2013:839–56. doi:10.1007/s00382-012-1337-9.
[53] Hayhoe K, Cayan D, Field CB, Frumhoff PC, Maurer EP, Miller NL, et al. Emissions pathways , climate change , and impacts on California. Proc Natl Acad Sci U S A 2004;101.
[54] Maurer EP, Hidalgo HG. Utility of daily vs . monthly large-scale climate data : an intercomparison of two statistical downscaling methods 2010:1125–38. doi:10.5194/hess-14-1125-2010.
[55] Themeßl MJ, Gobiet A, Leuprecht A. Empirical-statistical downscaling and error correction of daily precipitation from regional climate models. Int J Climatol 2011;1544:1530–44. doi:10.1002/joc.2168.
[56] Miao C, Su L, Sun Q, Duan Q. A nonstationary bias-correction technique to remove bias in GCM simulations. J Geophys Res Atmos 2015:5718–35. doi:10.1002/2015JD024159.
[57] CICS. Downscaling background. Frequently Asked Questions. Accessed 6/5/2013 2006. http://www.ipcc-data.org/ddc/ddc_faq.html.
[58] Dicksinson R, Rm Enico. A regional climate model for the western US. Clim Chang 1989. doi:10.1016/S0360-3199(01)00119-7.
[59] Ahmed KF. Bias Correction and Downscaling of Climate Model Outputs Required for Impact Assessments of Climate Change in the U . S . Northeast Bias Correction and Downscaling of Climate Model Master of Science 2011.
[60] The world bank group. Climate Change Knowledge Portal 2015. http://sdwebx.worldbank.org/climateportal/.
[61] Crawley DB, Shirey DB, Cornick SM, Jarrett PL, Lott JN, Morris RJ, et al. Climatic Data for Building Design Standards 2013. https://www.ashrae.org/technical-resources/bookstore/ashrae-climate-data-center.
[62] Jentsch MF, Bahaj AS, James PAB. Climate change future proofing of buildings—Generation and assessment of building simulation weather files. Energy Build 2008;40:2148–68.
[63] Wang X, Chen D, Ren Z. Assessment of climate change impact on residential building heating and cooling energy requirement in Australia. Build Environ 2010;45:1663–82. doi:10.1016/j.buildenv.2010.01.022.
[64] Chan ALS. Developing future hourly weather files for studying the impact of climate change on building energy performance in Hong Kong. Energy Build 2011;43:2860–8. doi:10.1016/j.enbuild.2011.07.003.
[65] Roshan GR, Orosa JA, Nasrabadi T. Simulation of climate change impact on energy consumption in buildings , case study of Iran. Energy Policy 2012;49:731–9. doi:10.1016/j.enpol.2012.07.020.
[66] Wan KKW, Li DHW, Pan W, Lam JC. Impact of climate change on building energy use in different climate zones and mitigation and adaptation implications. Appl Energy 2012;97:274–82. doi:10.1016/j.apenergy.2011.11.048.
[67] Dodoo A, Gustavsson L. Energy use and overheating risk of Swedish multi-storey residential buildings under different climate scenarios. Energy 2016;97:534–48. doi:10.1016/j.energy.2015.12.086.
[68] Invidiata A, Ghisi E. Impact of climate change on heating and cooling energy demand in houses in Brazil. Energy Build 2016;130:20–32. doi:10.1016/j.enbuild.2016.07.067.
[69] Sabunas A, Kanapickas A. Estimation of climate change impact on energy consumption in a residential building in Kaunas, Lithuania, using HEED Software. Energy Procedia 2017;128:92–9. doi:10.1016/j.egypro.2017.09.020.
[70] Du H, Edge J, Underwood C. Modelling the impacts of new UK future weather data on a school building. Proc Build Simul 2011.
[71] Shibuya T, Croxford B. The effect of climate change on office building energy consumption in Japan. Energy Build 2016;117:149–59. doi:10.1016/j.enbuild.2016.02.023.
[72] Robert A, Kummert M. Designing net-zero energy buildings for the future climate , not for the past. Build Environ 2012;55:150–8. doi:10.1016/j.buildenv.2011.12.014.
[73] Nik VM, Kalagasidis AS. Impact study of the climate change on the energy performance of the building stock in Stockholm considering four climate uncertainties. Build Environ 2013;60:291–304. doi:10.1016/j.buildenv.2012.11.005.
[74] Alves CA, Duarte DHS, Gonc FLT. Residential buildings ’ thermal performance and comfort for the elderly under climate changes context in the city of São Paulo , Brazil. Energy Build 2016;114:62–71. doi:10.1016/j.enbuild.2015.06.044.
[75] Taylor ZT. Climate Change Impacts on Residential and Commercial Loads in the Western U . S . Grid. IEEE Trans Power Syst 2008;25:6547.
[76] Gomes N, Pina A, Ferrão P, Fournier J, Lacarrière B, Corre O Le. Modeling the long-term effect of climate change on building heat demand : Case study on a district level. Energy Build 2016;126:77–93. doi:10.1016/j.enbuild.2016.04.082.
[77] Olonscheck M, Holsten A, Kropp JP. Heating and cooling energy demand and related emissions of the German residential building stock under climate change. Energy Policy 2011;39:4795–806. doi:10.1016/j.enpol.2011.06.041.
[78] Filippín C, Ricard F, Flores Larsen S, Santamouris M. Retrospective analysis of the energy consumption of single-family dwellings in central Argentina. Retrofitting and adaptation to the climate change. Renew Energy 2017;101:1226–41. doi:10.1016/j.renene.2016.09.064.
[79] Karimpour M, Belusko M, Xing K, Boland J, Bruno F. Impact of climate change on the design of energy efficient residential building envelopes. Energy Build 2015;87:142–54. doi:10.1016/j.enbuild.2014.10.064.
[80] Wong SL, Wan KKW, Li DHW, Lam JC. Impact of climate change on residential building envelope cooling loads in subtropical climates. Energy Build 2010;42:2098–103. doi:10.1016/j.enbuild.2010.06.021.
[81] Dirks JA, Gorrissen WJ, Hathaway JH, Skorski DC, Scott MJ, Pulsipher TC, et al. Impacts of climate change on energy consumption and peak demand in buildings: A detailed regional approach. Energy 2015;79:20–32. doi:10.1016/j.energy.2014.08.081.
[82] CelinaFilippín, FlorenciaRicard, Larsen S, MattheosSantamouris. Retrospective analysis of the energy consumption of single-family dwellings in central Argentina. Retrofitting and adaptation to the climate change. Renew Energy 2017;101:1226–41.
[83] National snow and ice data centre. Quick Facts on Arctic Sea Ice 2018. https://nsidc.org/cryosphere/quickfacts/seaice.html.
[84] Bastani A, Haghighat F, Jalon C. Investigating the effect of control strategy on the shift of energy consumption in a building integrated with PCM wallboard. Energy Procedia 2015;78:2280–5. doi:10.1016/j.egypro.2015.11.365.
[85] Sumaila P. Contrôle du chauffage pour la gestion de la demande résidentielle Rapport technique sur la création d ’ un modèle résidentiel fonctionnel 2010.
[86] Yao W, Li Z, Lu Y, Jiang F, Li C. Proceedings of the 8th International Symposium on Heating, Ventilation and Air Conditioning. Springer 2014;261:653–63. doi:10.1007/978-3-642-39584-0.
[87] Liu BY, Richard C. Jordan. The interrelationship and characteristic distribution of direct, diffuse and total solar radiation. Sol Energy 1960;4.3:1–19.
[88] Paliaga G, Schoen LJ, Alspach PF, Arens EA, Aynsley RM, Bean R, et al. Thermal Environmental Conditions for Human Occupancy. ASHRAE Stand 2014;8400.
[89] Kattsov V, Federation R, Reason C, Africa S et al. Evaluation of Climate Models 9. Ipcc 2013.
[90] WCRP. CMIP5 - Coupled Model Intercomparison Project Phase 5 - Overview 2008. https://cmip.llnl.gov/cmip5/.
[91] Environment Canada. Engineering Climate Datasets 2017. http://climate.weather.gc.ca/prods_servs/engineering_e.html.
[92] Bennett JC, Grose MR, Corney SP, White CJ, Holz GK, Katzfey JJ, et al. Performance of an empirical bias-correction of a high-resolution climate dataset. Int J Climatol 2014;34:2189–204. doi:10.1002/joc.3830.
[93] Leif E. Peterson. K-nearest_neighbor. Scholarpedia 2009;4:1883.
[94] D.T.Reindl, W.A.Beckman, J.A.Duffie. Diffuse fraction correlations. Sol Energy 1990;45:1–7.
All items in Spectrum are protected by copyright, with all rights reserved. The use of items is governed by Spectrum's terms of access.
Repository Staff Only: item control page
Download Statistics
Download Statistics