References:

REFERENCES
[1] P. Bocquier, World urbanization prospects: An alternative to the UN model of projection compatible with the mobility transition theory, Demogr. Res. 12 (2005) 197–236. https://doi.org/10.4054/DemRes.2005.12.9.
[2] T.B. Williams, Microclimatic temperature relationships over different surfaces, J. Geog. (1991). https://doi.org/10.1080/00221349108979321.
[3] I. Orlanski, A rational subdivision of scales for atmospheric processes, Bull. Amer. Meteor. Soc. (1975).
[4] N. Antoniou, H. Montazeri, M. Neophytou, B. Blocken, CFD simulation of urban microclimate: Validation using high-resolution field measurements, Sci. Total Environ. 695 (2019) 133743. https://doi.org/10.1016/j.scitotenv.2019.133743.
[5] G. Chen, D. Wang, Q. Wang, Y. Li, X. Wang, J. Hang, P. Gao, C. Ou, K. Wang, Scaled outdoor experimental studies of urban thermal environment in street canyon models with various aspect ratios and thermal storage, Sci. Total Environ. (2020). https://doi.org/10.1016/j.scitotenv.2020.138147.
[6] Y. Toparlar, B. Blocken, B. Maiheu, G.J.F. van Heijst, A review on the CFD analysis of urban microclimate, Renew. Sustain. Energy Rev. 80 (2017) 1613–1640. https://doi.org/10.1016/j.rser.2017.05.248.
[7] B. Blocken, J. Carmeliet, Pedestrian wind environment around buildings: Literature review and practical examples, J. Therm. Envel. Build. Sci. 28 (2004) 107–159. https://doi.org/10.1177/1097196304044396.
[8] P. Moonen, T. Defraeye, V. Dorer, B. Blocken, J. Carmeliet, Urban Physics: Effect of the micro-climate on comfort, health and energy demand, Front. Archit. Res. 1 (2012) 197–228. https://doi.org/10.1016/j.foar.2012.05.002.
[9] L. Howard, The Climate of London, vol I, Harvey Dart. (1833).
[10] M. Santamouris, N. Papanikolaou, I. Livada, I. Koronakis, C. Georgakis, A. Argiriou, D.N. Assimakopoulos, On the impact of urban climate on the energy consuption of building, Sol. Energy. 70 (2001) 201–216. https://doi.org/10.1016/S0038-092X(00)00095-5.
[11] B.T. Yang, R.N. Meroney, ON DIFFUSION FROM AN INSTANTANEOUS POINT SOURCE IN A NEUTRALLY STRATIFIED TURBULENT BOUNDARY LAYER WITH A LASER LIGHT SCATTERING PROBE., Colo State Univ (Fort Collins), Proj THEMI. (1972).
[12] P.Y. Cui, Z. Li, W.Q. Tao, Wind-tunnel measurements for thermal effects on the air flow and pollutant dispersion through different scale urban areas, Build. Environ. 97 (2016) 137–151. https://doi.org/10.1016/j.buildenv.2015.12.010.
[13] M. Hadavi, H. Pasdarshahri, Impacts of urban buildings on microclimate and cooling systems efficiency: Coupled CFD and BES simulations, Sustain. Cities Soc. (2021). https://doi.org/10.1016/j.scs.2021.102740.
[14] M. Zeeshan, Z. Ali, Using a blue landscape to mitigate heat stress during a heatwave event: a simulation study in a hot-humid urban environment, J. Water Clim. Chang. (2023). https://doi.org/10.2166/wcc.2023.363.
[15] M. Mortezazadeh, L.L. Wang, M. Albettar, S. Yang, CityFFD – City fast fluid dynamics for urban microclimate simulations on graphics processing units, Urban Clim. (2022). https://doi.org/10.1016/j.uclim.2021.101063.
[16] C. Shu, A. Gaur, L. (Leon) Wang, M. Bartko, A. Laouadi, L. Ji, M. Lacasse, Added value of convection permitting climate modelling in urban overheating assessments, Build. Environ. (2022). https://doi.org/10.1016/j.buildenv.2021.108415.
[17] Z. Gao, Y. Hou, W. Chen, Enhanced sensitivity of the urban heat island effect to summer temperatures induced by urban expansion, Environ. Res. Lett. (2019). https://doi.org/10.1088/1748-9326/ab2740.
[18] F. Binarti, M.D. Koerniawan, S. Triyadi, S.S. Utami, A. Matzarakis, A review of outdoor thermal comfort indices and neutral ranges for hot-humid regions, Urban Clim. (2020). https://doi.org/10.1016/j.uclim.2019.100531.
[19] A. Abd Razak, A. Hagishima, N. Ikegaya, J. Tanimoto, Analysis of airflow over building arrays for assessment of urban wind environment, Build. Environ. (2013). https://doi.org/10.1016/j.buildenv.2012.08.007.
[20] F. Salata, I. Golasi, A.D.L. Vollaro, R.D.L. Vollaro, How high albedo and traditional buildings’ materials and vegetation affect the quality of urban microclimate. A case study, Energy Build. (2015). https://doi.org/10.1016/j.enbuild.2015.04.010.
[21] A. Mochida, I.Y.F. Lun, Prediction of wind environment and thermal comfort at pedestrian level in urban area, J. Wind Eng. Ind. Aerodyn. 96 (2008) 1498–1527. https://doi.org/10.1016/j.jweia.2008.02.033.
[22] J. Allegrini, V. Dorer, J. Carmeliet, Buoyant flows in street canyons: Validation of CFD simulations with wind tunnel measurements, Build. Environ. 72 (2014) 63–74. https://doi.org/10.1016/j.buildenv.2013.10.021.
[23] L.W. Chew, L.R. Glicksman, L.K. Norford, Buoyant flows in street canyons: Comparison of RANS and LES at reduced and full scales, Build. Environ. 146 (2018) 77–87. https://doi.org/10.1016/j.buildenv.2018.09.026.
[24] H. Bherwani, A. Singh, R. Kumar, Assessment methods of urban microclimate and its parameters: A critical review to take the research from lab to land, Urban Clim. (2020). https://doi.org/10.1016/j.uclim.2020.100690.
[25] U.K. Priya, R. Senthil, A review of the impact of the green landscape interventions on the urban microclimate of tropical areas, Build. Environ. (2021). https://doi.org/10.1016/j.buildenv.2021.108190.
[26] P. Ampatzidis, T. Kershaw, A review of the impact of blue space on the urban microclimate, Sci. Total Environ. (2020). https://doi.org/10.1016/j.scitotenv.2020.139068.
[27] Z.T. Ai, C.M. Mak, From street canyon microclimate to indoor environmental quality in naturally ventilated urban buildings: Issues and possibilities for improvement, Build. Environ. (2015). https://doi.org/10.1016/j.buildenv.2015.10.008.
[28] A. Shafaghat, G. Manteghi, A. Keyvanfar, H. Bin Lamit, K. Saito, D.R. Ossen, Street geometry factors influence urban microclimate in tropical coastal cities: A review, Environ. Clim. Technol. (2016). https://doi.org/10.1515/rtuect-2016-0006.
[29] D. Lai, W. Liu, T. Gan, K. Liu, Q. Chen, A review of mitigating strategies to improve the thermal environment and thermal comfort in urban outdoor spaces, Sci. Total Environ. (2019). https://doi.org/10.1016/j.scitotenv.2019.01.062.
[30] Z. Liu, K.Y. Cheng, Y. He, C.Y. Jim, R.D. Brown, Y. Shi, K. Lau, E. Ng, Microclimatic measurements in tropical cities: Systematic review and proposed guidelines, Build. Environ. 222 (2022) 109411. https://doi.org/10.1016/j.buildenv.2022.109411.
[31] T. Hu, Y. Hu, K. Qi, Dynamic Evolution of Surface Urban Heat Island in Beijing, in: Int. Geosci. Remote Sens. Symp., 2019. https://doi.org/10.1109/IGARSS.2019.8898624.
[32] D. Mu, N. Gao, T. Zhu, CFD investigation on the effects of wind and thermal wall-flow on pollutant transmission in a high-rise building, Build. Environ. (2018). https://doi.org/10.1016/j.buildenv.2018.03.051.
[33] S. Huttner, M. Bruse, Numerical modeling of the urban climate - a preview on ENVI-MET 4.0, Seventh Int. Conf. Urban Clim. (2009).
[34] M. Neophytou, A. Gowardhan, M. Brown, An inter-comparison of three urban wind models using Oklahoma City Joint Urban 2003 wind field measurements, J. Wind Eng. Ind. Aerodyn. (2011). https://doi.org/10.1016/j.jweia.2011.01.010.
[35] A.C.S. Lai, A.C.T. So, S.K.C. Ng, D. Jonas, The territory-wide airborne light detection and ranging survey for the Hong Kong special administrative region, in: 33rd Asian Conf. Remote Sens. 2012, ACRS 2012, 2012.
[36] R. Byrne, N.J. Hewitt, P. Griffiths, P. MacArtain, A comparison of four microscale wind flow models in predicting the real-world performance of a large-scale peri-urban wind turbine, using onsite LiDAR wind measurements, Sustain. Energy Technol. Assessments. (2021). https://doi.org/10.1016/j.seta.2021.101323.
[37] G. Battista, E. Carnielo, R. De Lieto Vollaro, Thermal impact of a redeveloped area on localized urban microclimate: A case study in Rome, Energy Build. (2016). https://doi.org/10.1016/j.enbuild.2016.10.004.
[38] W. Liu, Y. Zhang, Q. Deng, The effects of urban microclimate on outdoor thermal sensation and neutral temperature in hot-summer and cold-winter climate, Energy Build. (2016). https://doi.org/10.1016/j.enbuild.2016.06.086.
[39] M. Nikolopoulou, S. Lykoudis, Use of outdoor spaces and microclimate in a Mediterranean urban area, Build. Environ. (2007). https://doi.org/10.1016/j.buildenv.2006.09.008.
[40] N. Antoniou, H. Montazeri, M. Neophytou, B. Blocken, CFD simulation of urban microclimate: Validation using high-resolution field measurements, Sci. Total Environ. 695 (2019) 133743. https://doi.org/10.1016/j.scitotenv.2019.133743.
[41] L. Klok, S. Zwart, H. Verhagen, E. Mauri, The surface heat island of Rotterdam and its relationship with urban surface characteristics, Resour. Conserv. Recycl. (2012). https://doi.org/10.1016/j.resconrec.2012.01.009.
[42] A. M’Saouri El Bat, Z. Romani, E. Bozonnet, A. Draoui, Integration of a practical model to assess the local urban interactions in building energy simulation with a street canyon, J. Build. Perform. Simul. (2020). https://doi.org/10.1080/19401493.2020.1818829.
[43] M. Neophytou, P. Fokaides, I. Panagiotou, I. Ioannou, M. Petrou, M. Sandberg, H. Wigo, E. Linden, E. Batchvarova, P. Videnov, B. Dimitroff, A. Ivanov, Towards Optimization of Urban Planning and Architectural Parameters for Energy use Minimization in Mediterranean Cities, in: Proc. World Renew. Energy Congr. – Sweden, 8–13 May, 2011, Linköping, Sweden, 2011. https://doi.org/10.3384/ecp110573372.
[44] M. Doya, E. Bozonnet, F. Allard, Experimental measurement of cool facades’ performance in a dense urban environment, in: Energy Build., 2012. https://doi.org/10.1016/j.enbuild.2011.11.001.
[45] M. Shahrestani, R. Yao, Z. Luo, E. Turkbeyler, H. Davies, A field study of urban microclimates in London, Renew. Energy. (2015). https://doi.org/10.1016/j.renene.2014.05.061.
[46] G.K.L. Wong, C.Y. Jim, Urban-microclimate effect on vector mosquito abundance of tropical green roofs, Build. Environ. (2017). https://doi.org/10.1016/j.buildenv.2016.11.028.
[47] S. Tong, N.H. Wong, C.L. Tan, S.K. Jusuf, M. Ignatius, E. Tan, Impact of urban morphology on microclimate and thermal comfort in northern China, Sol. Energy. (2017). https://doi.org/10.1016/j.solener.2017.06.027.
[48] S.A. Zaki, N.E. Othman, S.W. Syahidah, F. Yakub, F. Muhammad-Sukki, J.A. Ardila-Rey, M.F. Shahidan, A.S.M. Saudi, Effects of urban morphology on microclimate parameters in an urban university campus, Sustain. (2020). https://doi.org/10.3390/su12072962.
[49] T. Hong, Y. Xu, K. Sun, W. Zhang, X. Luo, B. Hooper, Urban microclimate and its impact on building performance: A case study of San Francisco, Urban Clim. (2021). https://doi.org/10.1016/j.uclim.2021.100871.
[50] S.W. Kiyoshi Uehara, Shuzo Murakami, Susumu Oikawa, Wind tunnel experiments on how thermal stratification affects flow in and above urban street canyons, J. Wind Eng. Ind. Aerodyn. 94 (2006) 621–636. https://doi.org/http://dx.doi.org/10.1016/j.jweia.2006.02.003.
[51] Y. Ogawa, P.G. Diosey, K. Uehara, H. Ueda, A wind tunnel for studying the effects of thermal stratification in the atmosphere, Atmos. Environ. (1981). https://doi.org/10.1016/0004-6981(81)90286-9.
[52] J. Allegrini, V. Dorer, J. Carmeliet, Wind tunnel measurements of buoyant flows in street canyons, Build. Environ. (2013). https://doi.org/10.1016/j.buildenv.2012.08.029.
[53] J. Allegrini, A wind tunnel study on three-dimensional buoyant flows in street canyons with different roof shapes and building lengths, Build. Environ. (2018). https://doi.org/10.1016/j.buildenv.2018.06.056.
[54] Z. Mo, C.H. Liu, Wind tunnel measurements of pollutant plume dispersion over hypothetical urban areas, Build. Environ. (2018). https://doi.org/10.1016/j.buildenv.2018.01.046.
[55] T.S. Larsen, P. Heiselberg, Single-sided natural ventilation driven by wind pressure and temperature difference, Energy Build. (2008). https://doi.org/10.1016/j.enbuild.2006.07.012.
[56] W. Zhang, C.D. Markfort, F. Porté-Agel, Wind-Turbine Wakes in a Convective Boundary Layer: A Wind-Tunnel Study, Boundary-Layer Meteorol. (2013). https://doi.org/10.1007/s10546-012-9751-4.
[57] M.F. Yassin, A wind tunnel study on the effect of thermal stability on flow and dispersion of rooftop stack emissions in the near wake of a building, Atmos. Environ. (2013). https://doi.org/10.1016/j.atmosenv.2012.10.013.
[58] L. Zhang, Y. Feng, Q. Meng, Y. Zhang, Experimental study on the building evaporative cooling by using the Climatic Wind Tunnel, Energy Build. (2015). https://doi.org/10.1016/j.enbuild.2015.07.038.
[59] J. Huang, P. Jones, A. Zhang, R. Peng, X. Li, P. wai Chan, Urban Building Energy and Climate (UrBEC) simulation: Example application and field evaluation in Sai Ying Pun, Hong Kong, Energy Build. (2020). https://doi.org/10.1016/j.enbuild.2019.109580.
[60] Y. Lin, T. Ichinose, Y. Yamao, H. Mouri, Wind velocity and temperature fields under different surface heating conditions in a street canyon in wind tunnel experiments, Build. Environ. (2020). https://doi.org/10.1016/j.buildenv.2019.106500.
[61] Y. Jiang, D. Alexander, H. Jenkins, R. Arthur, Q. Chen, Natural ventilation in buildings: Measurement in a wind tunnel and numerical simulation with large-eddy simulation, J. Wind Eng. Ind. Aerodyn. (2003). https://doi.org/10.1016/S0167-6105(02)00380-X.
[62] J. Allegrini, J.H. Kämpf, V. Dorer, J. Carmeliet, Modelling the Urban Microclimate and its Influence on Building Energy Demands of an Urban Neighbourhood, in: Proc. CISBAT 2013 Cleantech Smart Cities Build., EPFL Solar Energy and Building Physics Laboratory (LESO-PB), Lausanne, Switzerland, 2013: pp. 867–872. http://infoscience.epfl.ch/record/195701.
[63] S.M. Salim, R. Buccolieri, A. Chan, S. Di Sabatino, Numerical simulation of atmospheric pollutant dispersion in an urban street canyon: Comparison between RANS and LES, J. Wind Eng. Ind. Aerodyn. (2011). https://doi.org/10.1016/j.jweia.2010.12.002.
[64] X. Zheng, H. Montazeri, B. Blocken, CFD simulations of wind flow and mean surface pressure for buildings with balconies: Comparison of RANS and LES, Build. Environ. (2020). https://doi.org/10.1016/j.buildenv.2020.106747.
[65] M. Shirzadi, P.A. Mirzaei, Y. Tominaga, CFD analysis of cross-ventilation flow in a group of generic buildings: Comparison between steady RANS, LES and wind tunnel experiments, Build. Simul. (2020). https://doi.org/10.1007/s12273-020-0657-7.
[66] ANSYS Fluent Theory Guide, ANSYS Fluent Theory Guide, ANSYS Inc., USA. (2020).
[67] M. Bruse, ENVI-met 3.0: Updated Model Overview, (2004) 1–12.
[68] N.G. Jacobsen, D.R. Fuhrman, J. Fredsøe, A wave generation toolbox for the open-source CFD library: OpenFoam®, Int. J. Numer. Methods Fluids. (2012). https://doi.org/10.1002/fld.2726.
[69] A. Kamal, S.M.H. Abidi, A. Mahfouz, S. Kadam, A. Rahman, I.G. Hassan, L.L. Wang, Impact of urban morphology on urban microclimate and building energy loads, Energy Build. (2021). https://doi.org/10.1016/j.enbuild.2021.111499.
[70] J. Pfafferott, S. Rißmann, M. Sühring, F. Kanani-Sühring, B. Maronga, Building indoor model in PALM-4U: Indoor climate, energy demand, and the interaction between buildings and the urban microclimate, Geosci. Model Dev. (2021). https://doi.org/10.5194/gmd-14-3511-2021.
[71] J. Vogel, A. Afshari, G. Chockalingam, S. Stadler, Evaluation of a novel WRF/PALM-4U coupling scheme incorporating a roughness-corrected surface layer representation, Urban Clim. (2022). https://doi.org/10.1016/j.uclim.2022.101311.
[72] J. Geletič, M. Lehnert, P. Krč, J. Resler, E.S. Krayenhoff, High-resolution modelling of thermal exposure during a hot spell: A case study using palm-4u in prague, czech republic, Atmosphere (Basel). (2021). https://doi.org/10.3390/atmos12020175.
[73] J. Liu, M. Heidarinejad, S.K. Nikkho, N.W. Mattise, J. Srebric, Quantifying impacts of urban microclimate on a building energy consumption-a case study, Sustain. (2019). https://doi.org/10.3390/su11184921.
[74] Z. Zhai, Q. Chen, P. Haves, J.H. Klems, On approaches to couple energy simulation and computational fluid dynamics programs, Build. Environ. (2002). https://doi.org/10.1016/S0360-1323(02)00054-9.
[75] J. Allegrini, V. Dorer, J. Carmeliet, Influence of the urban microclimate in street canyons on the energy demand for space cooling and heating of buildings, Energy Build. (2012). https://doi.org/10.1016/j.enbuild.2012.10.013.
[76] A. Katal, M. Mortezazadeh, L. (Leon) Wang, Modeling building resilience against extreme weather by integrated CityFFD and CityBEM simulations, Appl. Energy. 250 (2019) 1402–1417. https://doi.org/10.1016/j.apenergy.2019.04.192.
[77] Y. Miao, S. Liu, B. Chen, B. Zhang, S. Wang, S. Li, Simulating urban flow and dispersion in Beijing by coupling a CFD model with the WRF model, Adv. Atmos. Sci. (2013). https://doi.org/10.1007/s00376-013-2234-9.
[78] F. Miguet, D. Groleau, Urban bioclimatic indicators for urban planers with the software tool SOLENE, in: Port. SB 2007 - Sustain. Constr. Mater. Pract. Chall. Ind. New Millenn., 2007.
[79] R. Zhang, K.P. Lam, S. chune Yao, Y. Zhang, Coupled EnergyPlus and computational fluid dynamics simulation for natural ventilation, Build. Environ. (2013). https://doi.org/10.1016/j.buildenv.2013.04.002.
[80] K. Perini, A. Chokhachian, S. Dong, T. Auer, Modeling and simulating urban outdoor comfort: Coupling ENVI-Met and TRNSYS by grasshopper, Energy Build. (2017). https://doi.org/10.1016/j.enbuild.2017.07.061.
[81] J. Brozovsky, A. Simonsen, N. Gaitani, Validation of a CFD model for the evaluation of urban microclimate at high latitudes: A case study in Trondheim, Norway, Build. Environ. (2021). https://doi.org/10.1016/j.buildenv.2021.108175.
[82] N. Fintikakis, N. Gaitani, M. Santamouris, M. Assimakopoulos, D.N. Assimakopoulos, M. Fintikaki, G. Albanis, K. Papadimitriou, E. Chryssochoides, K. Katopodi, P. Doumas, Bioclimatic design of open public spaces in the historic centre of Tirana, Albania, Sustain. Cities Soc. (2011). https://doi.org/10.1016/j.scs.2010.12.001.
[83] M.F. Shahidan, P.J. Jones, J. Gwilliam, E. Salleh, An evaluation of outdoor and building environment cooling achieved through combination modification of trees with ground materials, Build. Environ. (2012). https://doi.org/10.1016/j.buildenv.2012.07.012.
[84] Y. Toparlar, B. Blocken, P. Vos, G.J.F. Van Heijst, W.D. Janssen, T. van Hooff, H. Montazeri, H.J.P. Timmermans, CFD simulation and validation of urban microclimate: A case study for Bergpolder Zuid, Rotterdam, Build. Environ. (2015). https://doi.org/10.1016/j.buildenv.2014.08.004.
[85] X. Yang, L. Zhao, M. Bruse, Q. Meng, Evaluation of a microclimate model for predicting the thermal behavior of different ground surfaces, Build. Environ. (2013). https://doi.org/10.1016/j.buildenv.2012.11.008.
[86] J. Ma, X. Li, Y. Zhu, A simplified method to predict the outdoor thermal environment in residential district, Build. Simul. (2012). https://doi.org/10.1007/s12273-012-0079-2.
[87] J. Brozovsky, S. Corio, N. Gaitani, A. Gustavsen, Evaluation of sustainable strategies and design solutions at high-latitude urban settlements to enhance outdoor thermal comfort, Energy Build. (2021). https://doi.org/10.1016/j.enbuild.2021.111037.
[88] U. Berardi, Y. Wang, The effect of a denser city over the urban microclimate: The case of Toronto, Sustain. (2016). https://doi.org/10.3390/su8080822.
[89] Y. Tominaga, Y. Sato, S. Sadohara, CFD simulations of the effect of evaporative cooling from water bodies in a micro-scale urban environment: Validation and application studies, Sustain. Cities Soc. 19 (2015) 259–270. https://doi.org/10.1016/j.scs.2015.03.011.
[90] M. Mortezazadeh, Z. Jandaghian, L.L. Wang, Integrating CityFFD and WRF for modeling urban microclimate under heatwaves, Sustain. Cities Soc. (2021). https://doi.org/10.1016/j.scs.2020.102670.
[91] D. Clarke, R. Kinghorn, A. Cam, M. Chui, B. Hall, N. Robert, R. STEFAN, G. CARUTASU, S. Caner, F. Bhatti, H. Michael, K. Andreas, N.R. Mosteanu, S. Weber, A Brief History of Artificial Intelligence: On the Past, Present, and Futur...: Search KCenter resources, McKinsey. (2019).
[92] K. Häb, A. Middel, B.L. Ruddell, H. Hagen, A Data-Driven Approach to Categorize Climatic Microenvironments, in: EnvirVis 2016 - Work. Vis. Environ. Sci., 2016. https://doi.org/10.2312/envirvis.20161105.
[93] L. Alonso, F. Renard, A new approach for understanding urban microclimate by integrating complementary predictors at different scales in regression and machine learning models, Remote Sens. (2020). https://doi.org/10.3390/RS12152434.
[94] M. Zhang, X. Zhang, S. Guo, X. Xu, J. Chen, W. Wang, Urban micro-climate prediction through long short-term memory network with long-term monitoring for on-site building energy estimation, Sustain. Cities Soc. (2021). https://doi.org/10.1016/j.scs.2021.103227.
[95] S. Higgins, T. Stathopoulos, Application of artificial intelligence to urban wind energy, Build. Environ. (2021). https://doi.org/10.1016/j.buildenv.2021.107848.
[96] G.Y. Oukawa, P. Krecl, A.C. Targino, Fine-scale modeling of the urban heat island: A comparison of multiple linear regression and random forest approaches, Sci. Total Environ. 815 (2022) 152836. https://doi.org/10.1016/j.scitotenv.2021.152836.
[97] C. Ding, K.P. Lam, Data-driven model for cross ventilation potential in high-density cities based on coupled CFD simulation and machine learning, Build. Environ. (2019). https://doi.org/10.1016/j.buildenv.2019.106394.
[98] N. Nazarian, J. Fan, T. Sin, L. Norford, J. Kleissl, Predicting outdoor thermal comfort in urban environments: A 3D numerical model for standard effective temperature, Urban Clim. (2017). https://doi.org/10.1016/j.uclim.2017.04.011.
[99] M. Mortezazadeh, J. Zou, M. Hosseini, S. Yang, L. Wang, Estimating Urban Wind Speeds and Wind Power Potentials Based on Machine Learning with City Fast Fluid Dynamics Training Data, Atmosphere (Basel). (2022). https://doi.org/10.3390/atmos13020214.
[100] Y. Yao, C. Chang, F. Ndayisaba, S. Wang, A new approach for surface urban heat island monitoring based on machine learning algorithm and spatiotemporal fusion model, IEEE Access. (2020). https://doi.org/10.1109/ACCESS.2020.3022047.
[101] I. Abohela, N. Hamza, S. Dudek, Effect of roof shape, wind direction, building height and urban configuration on the energy yield and positioning of roof mounted wind turbines, Renew. Energy. (2013). https://doi.org/10.1016/j.renene.2012.08.068.
[102] J. Liu, J. Niu, Y. Du, C.M. Mak, Y. Zhang, LES for pedestrian level wind around an idealized building array—Assessment of sensitivity to influencing parameters, Sustain. Cities Soc. (2019). https://doi.org/10.1016/j.scs.2018.10.034.
[103] Q.M. Zahid Iqbal, A.L.S. Chan, Pedestrian level wind environment assessment around group of high-rise cross-shaped buildings: Effect of building shape, separation and orientation, Build. Environ. (2016). https://doi.org/10.1016/j.buildenv.2016.02.015.
[104] W.D. Janssen, B. Blocken, T. van Hooff, Pedestrian wind comfort around buildings: Comparison of wind comfort criteria based on whole-flow field data for a complex case study, Build. Environ. 59 (2013) 547–562. https://doi.org/10.1016/j.buildenv.2012.10.012.
[105] A. Khalilzadeh, H. Ge, H.D. Ng, Effect of turbulence modeling schemes on wind-driven rain deposition on a mid-rise building: CFD modeling and validation, J. Wind Eng. Ind. Aerodyn. (2019). https://doi.org/10.1016/j.jweia.2018.11.012.
[106] K. Pettersson, S. Krajnovic, A.S. Kalagasidis, P. Johansson, Simulating wind-driven rain on building facades using Eulerian multiphase with rain phase turbulence model, Build. Environ. (2016). https://doi.org/10.1016/j.buildenv.2016.06.012.
[107] C. Yuan, L. Norford, E. Ng, A semi-empirical model for the effect of trees on the urban wind environment, Landsc. Urban Plan. (2017). https://doi.org/10.1016/j.landurbplan.2017.09.029.
[108] M.G. Giometto, A. Christen, P.E. Egli, M.F. Schmid, R.T. Tooke, N.C. Coops, M.B. Parlange, Effects of trees on mean wind, turbulence and momentum exchange within and above a real urban environment, Adv. Water Resour. (2017). https://doi.org/10.1016/j.advwatres.2017.06.018.
[109] G. Kang, J.J. Kim, W. Choi, Computational fluid dynamics simulation of tree effects on pedestrian wind comfort in an urban area, Sustain. Cities Soc. (2020). https://doi.org/10.1016/j.scs.2020.102086.
[110] P. Höppe, The physiological equivalent temperature - A universal index for the biometeorological assessment of the thermal environment, Int. J. Biometeorol. (1999). https://doi.org/10.1007/s004840050118.
[111] D. Fiala, G. Havenith, P. Bröde, B. Kampmann, G. Jendritzky, UTCI-Fiala multi-node model of human heat transfer and temperature regulation, Int. J. Biometeorol. (2012). https://doi.org/10.1007/s00484-011-0424-7.
[112] P. Kumar, A. Sharma, Study on importance, procedure, and scope of outdoor thermal comfort –A review, Sustain. Cities Soc. (2020). https://doi.org/10.1016/j.scs.2020.102297.
[113] H.M. Imran, J. Kala, A.W.M. Ng, S. Muthukumaran, Effectiveness of vegetated patches as Green Infrastructure in mitigating Urban Heat Island effects during a heatwave event in the city of Melbourne, Weather Clim. Extrem. (2019). https://doi.org/10.1016/j.wace.2019.100217.
[114] Y. Jamei, P. Rajagopalan, Q. (Chayn) Sun, Spatial structure of surface urban heat island and its relationship with vegetation and built-up areas in Melbourne, Australia, Sci. Total Environ. (2019). https://doi.org/10.1016/j.scitotenv.2018.12.308.
[115] H. Li, Y. Zhou, X. Wang, X. Zhou, H. Zhang, S. Sodoudi, Quantifying urban heat island intensity and its physical mechanism using WRF/UCM, Sci. Total Environ. (2019). https://doi.org/10.1016/j.scitotenv.2018.10.025.
[116] Y. Li, S. Schubert, J.P. Kropp, D. Rybski, On the influence of density and morphology on the Urban Heat Island intensity, Nat. Commun. (2020). https://doi.org/10.1038/s41467-020-16461-9.
[117] C. Ren, K. Wang, Y. Shi, Y.T. Kwok, T.E. Morakinyo, T. cheung Lee, Y. Li, Investigating the urban heat and cool island effects during extreme heat events in high-density cities: A case study of Hong Kong from 2000 to 2018, Int. J. Climatol. (2021). https://doi.org/10.1002/joc.7222.
[118] F. Salamanca, M. Georgescu, A. Mahalov, M. Moustaoui, M. Wang, Anthropogenic heating of the urban environment due to air conditioning, J. Geophys. Res. (2014). https://doi.org/10.1002/2013JD021225.
[119] S.W. Kim, R.D. Brown, Urban heat island (UHI) intensity and magnitude estimations: A systematic literature review, Sci. Total Environ. (2021). https://doi.org/10.1016/j.scitotenv.2021.146389.
[120] J. Kong, Y. Zhao, J. Carmeliet, C. Lei, Urban heat island and its interaction with heatwaves: A review of studies on mesoscale, Sustain. (2021). https://doi.org/10.3390/su131910923.
[121] G. Xian, H. Shi, R. Auch, K. Gallo, Q. Zhou, Z. Wu, M. Kolian, The effects of urban land cover dynamics on urban heat Island intensity and temporal trends, GIScience Remote Sens. (2021). https://doi.org/10.1080/15481603.2021.1903282.
[122] K.B. Moffett, Y. Makido, V. Shandas, Urban-rural surface temperature deviation and intra-urban variations contained by an urban growth boundary, Remote Sens. (2019). https://doi.org/10.3390/rs11222683.
[123] R. Yao, L. Wang, X. Huang, W. Gong, X. Xia, Greening in Rural Areas Increases the Surface Urban Heat Island Intensity, Geophys. Res. Lett. (2019). https://doi.org/10.1029/2018GL081816.
[124] D. Zhou, S. Zhao, S. Liu, L. Zhang, C. Zhu, Surface urban heat island in China’s 32 major cities: Spatial patterns and drivers, Remote Sens. Environ. (2014). https://doi.org/10.1016/j.rse.2014.05.017.
[125] S.I. Bohnenstengel, S. Evans, P.A. Clark, S.E. Belcher, Simulations of the London urban heat island, Q. J. R. Meteorol. Soc. (2011). https://doi.org/10.1002/qj.855.
[126] S. Peng, S. Piao, P. Ciais, P. Friedlingstein, C. Ottle, F.M. Bréon, H. Nan, L. Zhou, R.B. Myneni, Surface urban heat island across 419 global big cities, Environ. Sci. Technol. (2012). https://doi.org/10.1021/es2030438.
[127] M. Santamouris, Analyzing the heat island magnitude and characteristics in one hundred Asian and Australian cities and regions, Sci. Total Environ. (2015). https://doi.org/10.1016/j.scitotenv.2015.01.060.
[128] A. Dimoudi, A. Kantzioura, S. Zoras, C. Pallas, P. Kosmopoulos, Investigation of urban microclimate parameters in an urban center, Energy Build. (2013). https://doi.org/10.1016/j.enbuild.2013.04.014.
[129] F. Peron, M.M. De Maria, F. Spinazzè, U. Mazzali, An analysis of the urban heat island of Venice mainland, Sustain. Cities Soc. (2015). https://doi.org/10.1016/j.scs.2015.05.008.
[130] P. Rajagopalan, K.C. Lim, E. Jamei, Urban heat island and wind flow characteristics of a tropical city, Sol. Energy. (2014). https://doi.org/10.1016/j.solener.2014.05.042.
[131] E.L. Ndetto, A. Matzarakis, Effects of urban configuration on human thermal conditions in a typical tropical African coastal city, Adv. Meteorol. (2013). https://doi.org/10.1155/2013/549096.
[132] C.A.Souch, C.Souch, The effect of trees on summertime below canopy urban climates: a case study Bloomington, Indiana, J. Arboric. (1993).
[133] A. Aboelata, S. Sodoudi, Evaluating the effect of trees on UHI mitigation and reduction of energy usage in different built up areas in Cairo, Build. Environ. (2020). https://doi.org/10.1016/j.buildenv.2019.106490.
[134] X.X. Li, L.K. Norford, Evaluation of cool roof and vegetations in mitigating urban heat island in a tropical city, Singapore, Urban Clim. (2016). https://doi.org/10.1016/j.uclim.2015.12.002.
[135] M.O. Mughal, A. Kubilay, S. Fatichi, N. Meili, J. Carmeliet, P. Edwards, P. Burlando, Detailed investigation of vegetation effects on microclimate by means of computational fluid dynamics (CFD) in a tropical urban environment, Urban Clim. (2021). https://doi.org/10.1016/j.uclim.2021.100939.
[136] P. Ramamurthy, E. Bou-Zeid, Heatwaves and urban heat islands: A comparative analysis of multiple cities, J. Geophys. Res. (2017). https://doi.org/10.1002/2016JD025357.
[137] D. Li, E. Bou-Zeid, Synergistic interactions between urban heat islands and heat waves: The impact in cities is larger than the sum of its parts, J. Appl. Meteorol. Climatol. (2013). https://doi.org/10.1175/JAMC-D-13-02.1.
[138] D. Founda, M. Santamouris, Synergies between Urban Heat Island and Heat Waves in Athens (Greece), during an extremely hot summer (2012), Sci. Rep. (2017). https://doi.org/10.1038/s41598-017-11407-6.
[139] D. Founda, F. Pierros, M. Petrakis, C. Zerefos, Interdecadal variations and trends of the Urban Heat Island in Athens (Greece) and its response to heat waves, Atmos. Res. (2015). https://doi.org/10.1016/j.atmosres.2015.03.016.
[140] S. Jiang, X. Lee, J. Wang, K. Wang, Amplified Urban Heat Islands during Heat Wave Periods, J. Geophys. Res. Atmos. (2019). https://doi.org/10.1029/2018JD030230.
[141] L.W. Chew, X. Liu, X.X. Li, L.K. Norford, Interaction between heat wave and urban heat island: A case study in a tropical coastal city, Singapore, Atmos. Res. (2021). https://doi.org/10.1016/j.atmosres.2020.105134.
[142] A. Dimoudi, S. Zoras, A. Kantzioura, X. Stogiannou, P. Kosmopoulos, C. Pallas, Use of cool materials and other bioclimatic interventions in outdoor places in order to mitigate the urban heat island in a medium size city in Greece, Sustain. Cities Soc. (2014). https://doi.org/10.1016/j.scs.2014.04.003.
[143] J.P. Gastellu-Etchegorry, F. Zagolski, J. Romier, A simple anisotropic reflectance model for homogeneous multilayer canopies, Remote Sens. Environ. (1996). https://doi.org/10.1016/0034-4257(95)00221-9.
[144] F. Lindberg, B. Holmer, S. Thorsson, SOLWEIG 1.0 - Modelling spatial variations of 3D radiant fluxes and mean radiant temperature in complex urban settings, Int. J. Biometeorol. (2008). https://doi.org/10.1007/s00484-008-0162-7.
[145] V. Dorer, J. Allegrini, K. Orehounig, P. Moonen, G. Upadhyay, J. Kämpf, J. Carmeliet, Modelling the urban microclimate and its impact on the energy demand of buildings and building clusters, in: Proc. BS 2013 13th Conf. Int. Build. Perform. Simul. Assoc., 2013.
[146] Y. Toparlar, B. Blocken, B. Maiheu, G.J.F. van Heijst, Impact of urban microclimate on summertime building cooling demand: A parametric analysis for Antwerp, Belgium, Appl. Energy. (2018). https://doi.org/10.1016/j.apenergy.2018.06.110.
[147] L. Malys, M. Musy, C. Inard, A hydrothermal model to assess the impact of green walls on urban microclimate and building energy consumption, Build. Environ. (2014). https://doi.org/10.1016/j.buildenv.2013.12.012.
[148] M. Shirzadi, M. Naghashzadegan, P. A. Mirzaei, Improving the CFD modelling of cross-ventilation in highly-packed urban areas, Sustain. Cities Soc. (2018). https://doi.org/10.1016/j.scs.2017.11.020.
[149] M. Shirzadi, Y. Tominaga, P.A. Mirzaei, Wind tunnel experiments on cross-ventilation flow of a generic sheltered building in urban areas, Build. Environ. (2019). https://doi.org/10.1016/j.buildenv.2019.04.057.
[150] M. Shirzadi, Y. Tominaga, P.A. Mirzaei, Experimental and steady-RANS CFD modelling of cross-ventilation in moderately-dense urban areas, Sustain. Cities Soc. (2020). https://doi.org/10.1016/j.scs.2019.101849.
[151] Y. Tominaga, T. Stathopoulos, CFD simulation of near-field pollutant dispersion in the urban environment: A review of current modeling techniques, Atmos. Environ. (2013). https://doi.org/10.1016/j.atmosenv.2013.07.028.
[152] B. Blocken, Y. Tominaga, T. Stathopoulos, CFD simulation of micro-scale pollutant dispersion in the built environment, Build. Environ. (2013). https://doi.org/10.1016/j.buildenv.2013.01.001.
[153] P. Gousseau, B. Blocken, T. Stathopoulos, G.J.F. van Heijst, CFD simulation of near-field pollutant dispersion on a high-resolution grid: A case study by LES and RANS for a building group in downtown Montreal, Atmos. Environ. 45 (2011) 428–438. https://doi.org/10.1016/j.atmosenv.2010.09.065.
[154] S. Yang, L. (Leon) Wang, P. Raftery, M. Ivanovich, C. Taber, W.P. Bahnfleth, P. Wargocki, J. Pantelic, J. Zou, M. Mortezazadeh, C. Shu, R. Wang, S. Arnold, Comparing airborne infectious aerosol exposures in sparsely occupied large spaces utilizing large-diameter ceiling fans, Build. Environ. (2023). https://doi.org/10.1016/j.buildenv.2023.110022.
[155] M. Chavez, B. Hajra, T. Stathopoulos, A. Bahloul, Near-field pollutant dispersion in the built environment by CFD and wind tunnel simulations, J. Wind Eng. Ind. Aerodyn. 99 (2011) 330–339. https://doi.org/10.1016/j.jweia.2011.01.003.
[156] L. Soulhac, P. Salizzoni, P. Mejean, R.J. Perkins, Parametric laws to model urban pollutant dispersion with a street network approach, Atmos. Environ. (2013). https://doi.org/10.1016/j.atmosenv.2012.10.053.
[157] M. Carpentieri, P. Hayden, A.G. Robins, Wind tunnel measurements of pollutant turbulent fluxes in urban intersections, Atmos. Environ. (2012). https://doi.org/10.1016/j.atmosenv.2011.09.083.
[158] C. Yuan, E. Ng, L.K. Norford, Improving air quality in high-density cities by understanding the relationship between air pollutant dispersion and urban morphologies, Build. Environ. (2014). https://doi.org/10.1016/j.buildenv.2013.10.008.
[159] J.F. Sini, S. Anquetin, P.G. Mestayer, Pollutant dispersion and thermal effects in urban street canyons, Atmos. Environ. (1996). https://doi.org/10.1016/1352-2310(95)00321-5.
[160] B. Blocken, Computational Fluid Dynamics for urban physics: Importance, scales, possibilities, limitations and ten tips and tricks towards accurate and reliable simulations, Build. Environ. 91 (2015) 219–245. https://doi.org/10.1016/j.buildenv.2015.02.015.
[161] B. Blocken, 50 years of Computational Wind Engineering: Past, present and future, J. Wind Eng. Ind. Aerodyn. (2014). https://doi.org/10.1016/j.jweia.2014.03.008.
[162] A. Zhang, C. Gao, L. Zhang, Numerical simulation of the wind field around different building arrangements, J. Wind Eng. Ind. Aerodyn. 93 (2005) 891–904. https://doi.org/10.1016/j.jweia.2005.09.001.
[163] Y. Jiang, Q. Chen, Effect of fluctuating wind direction on cross natural ventilation in buildings from large eddy simulation, Build. Environ. (2002). https://doi.org/10.1016/S0360-1323(01)00036-1.
[164] Y. Tominaga, R. Yoshie, A. Mochida, H. Kataoka, K. Harimoto, T. Nozu, Cross Comparisons of CFD Prediction for Wind Environment at Pedestrian Level around Buildings Part 2 : Comparison of Results for Flowfield around Building Complex in Actual Urban Area, Sixth Asia-Pacific Conf. Wind Eng. (2005).
[165] Y. Tominaga, A. Mochida, R. Yoshie, H. Kataoka, T. Nozu, M. Yoshikawa, T. Shirasawa, AIJ guidelines for practical applications of CFD to pedestrian wind environment around buildings, J. Wind Eng. Ind. Aerodyn. 96 (2008) 1749–1761. https://doi.org/10.1016/j.jweia.2008.02.058.
[166] M. Mortezazadeh, L. (Leon) Wang, Solving city and building microclimates by fast fluid dynamics with large timesteps and coarse meshes, Build. Environ. (2020). https://doi.org/10.1016/j.buildenv.2020.106955.
[167] A. Katal, M. Mortezazadeh, L. (Leon) Wang, Modeling building resilience against extreme weather by integrated CityFFD and CityBEM simulations, Appl. Energy. (2019). https://doi.org/10.1016/j.apenergy.2019.04.192.
[168] J. Hensen, Modelling Coupled Heat and Air Flow: Ping-Pong vs Onions, IEA Air Infiltration Vent. (1995).
[169] J.M. Masterton, F.A. Richardson, C.E. Canada, C.A.E. Service, Humidex: A Method of Quantifying Human Discomfort Due to Excessive Heat and Humidity, Environment Canada, Atmospheric Environment, 1979. https://books.google.ca/books?id=lVETzQEACAAJ.
[170] L. Monteiro, M. Alucci, Outdoor thermal comfort: comparison of results of empirical field research and predictive models simulation, in: Comf. Energy Use Build. Get. It Right, 2006.
[171] É. Mekis, L.A. Vincent, M.W. Shephard, X. Zhang, Observed Trends in Severe Weather Conditions Based on Humidex, Wind Chill, and Heavy Rainfall Events in Canada for 1953-2012, Atmos. - Ocean. 53 (2015) 383–397. https://doi.org/10.1080/07055900.2015.1086970.
[172] J.M. Sobstyl, T. Emig, M.J.A. Qomi, F.J. Ulm, R.J.M. Pellenq, Role of City Texture in Urban Heat Islands at Nighttime, Phys. Rev. Lett. (2018). https://doi.org/10.1103/PhysRevLett.120.108701.
[173] L. Ji, A. Laouadi, C. Shu, A. Gaur, M. Lacasse, L. (Leon) Wang, Evaluating approaches of selecting extreme hot years for assessing building overheating conditions during heatwaves, Energy Build. (2022). https://doi.org/10.1016/j.enbuild.2021.111610.
[174] C. Shu, A. Gaur, L. Wang, M.A. Lacasse, Evolution of the local climate in Montreal and Ottawa before, during and after a heatwave and the effects on urban heat islands, Sci. Total Environ. (2023). https://doi.org/10.1016/j.scitotenv.2023.164497.
[175] L.H.U.W. Abeydeera, J.W. Mesthrige, T.I. Samarasinghalage, Global research on carbon emissions: A scientometric review, Sustain. (2019). https://doi.org/10.3390/su11143972.
[176] Z. Zeng, X. Zhou, L. Li, The Impact of Water on Microclimate in Lingnan Area, in: Procedia Eng., 2017. https://doi.org/10.1016/j.proeng.2017.10.082.
[177] S. Yang, L. (Leon) Wang, T. Stathopoulos, A.M. Marey, Urban microclimate and its impact on built environment – A review, Build. Environ. 238 (2023) 110334. https://doi.org/10.1016/j.buildenv.2023.110334.
[178] K. Lundgren, T. Kjellstrom, Sustainability challenges from climate change and air conditioning use in urban areas, Sustain. (2013). https://doi.org/10.3390/su5073116.
[179] J. Allegrini, V. Dorer, J. Carmeliet, Coupled CFD, radiation and building energy model for studying heat fluxes in an urban environment with generic building configurations, Sustain. Cities Soc. (2015). https://doi.org/10.1016/j.scs.2015.07.009.
[180] A. Aboelata, Vegetation in different street orientations of aspect ratio (H/W 1:1) to mitigate UHI and reduce buildings’ energy in arid climate, Build. Environ. (2020). https://doi.org/10.1016/j.buildenv.2020.106712.
[181] A. Katal, S. Leroyer, J. Zou, O. Nikiema, M. Albettar, S. Belair, L. (Leon) Wang, Outdoor heat stress assessment using an integrated multi-scale numerical weather prediction system: A case study of a heatwave in Montreal, Sci. Total Environ. (2023). https://doi.org/10.1016/j.scitotenv.2022.161276.
[182] X. Li, Y. Zhou, S. Yu, G. Jia, H. Li, W. Li, Urban heat island impacts on building energy consumption: A review of approaches and findings, Energy. (2019). https://doi.org/10.1016/j.energy.2019.02.183.
[183] M. Palme, L. Inostroza, G. Villacreses, A. Lobato, C. Carrasco, Urban weather data and building models for the inclusion of the urban heat island effect in building performance simulation, Data Br. (2017). https://doi.org/10.1016/j.dib.2017.08.035.
[184] N. Sezer, H. Yoonus, D. Zhan, L. (Leon) Wang, I.G. Hassan, M.A. Rahman, Urban microclimate and building energy models: A review of the latest progress in coupling strategies, Renew. Sustain. Energy Rev. (2023). https://doi.org/10.1016/j.rser.2023.113577.
[185] N. Lauzet, A. Rodler, M. Musy, M.H. Azam, S. Guernouti, D. Mauree, T. Colinart, How building energy models take the local climate into account in an urban context – A review, Renew. Sustain. Energy Rev. (2019). https://doi.org/10.1016/j.rser.2019.109390.
[186] S. Tsoka, K. Tolika, T. Theodosiou, K. Tsikaloudaki, D. Bikas, A method to account for the urban microclimate on the creation of ‘typical weather year’ datasets for building energy simulation, using stochastically generated data, Energy Build. (2018). https://doi.org/10.1016/j.enbuild.2018.01.016.
[187] S. Moghanlo, M. Alavinejad, V. Oskoei, H. Najafi Saleh, A.A. Mohammadi, H. Mohammadi, Z. DerakhshanNejad, Using artificial neural networks to model the impacts of climate change on dust phenomenon in the Zanjan region, north-west Iran, Urban Clim. (2021). https://doi.org/10.1016/j.uclim.2020.100750.
[188] Y. Xie, W. Hu, X. Zhou, S. Yan, C. Li, Artificial Neural Network Modeling for Predicting and Evaluating the Mean Radiant Temperature around Buildings on Hot Summer Days, Buildings. (2022). https://doi.org/10.3390/buildings12050513.
[189] B. Shboul, I. AL-Arfi, S. Michailos, D. Ingham, L. Ma, K.J. Hughes, M. Pourkashanian, A new ANN model for hourly solar radiation and wind speed prediction: A case study over the north & south of the Arabian Peninsula, Sustain. Energy Technol. Assessments. (2021). https://doi.org/10.1016/j.seta.2021.101248.
[190] D.B. Crawley, L.K. Lawrie, F.C. Winkelmann, W.F. Buhl, Y.J. Huang, C.O. Pedersen, R.K. Strand, R.J. Liesen, D.E. Fisher, M.J. Witte, J. Glazer, EnergyPlus: creating a new-generation building energy simulation program, Energy Build. 33 (2001) 319–331. https://doi.org/10.1016/S0378-7788(00)00114-6.
[191] C.Y. Siu, Z. Liao, Is building energy simulation based on TMY representative: A comparative simulation study on doe reference buildings in Toronto with typical year and historical year type weather files, Energy Build. (2020). https://doi.org/10.1016/j.enbuild.2020.109760.
[192] J. Zhang, F. Zhang, Z. Gou, J. Liu, Assessment of macroclimate and microclimate effects on outdoor thermal comfort via artificial neural network models, Urban Clim. (2022). https://doi.org/10.1016/j.uclim.2022.101134.
[193] X. Wu, J. Hou, J. Hui, Z. Tang, W. Wang, Revealing Microclimate around Buildings with Long-Term Monitoring through the Neural Network Algorithms, Buildings. (2022). https://doi.org/10.3390/buildings12040395.
[194] M. Mangiameli, G. Mussumeci, A. Gagliano, Evaluation of the Urban Microclimate in Catania using Multispectral Remote Sensing and GIS Technology, Climate. (2022). https://doi.org/10.3390/cli10020018.
[195] C. Shu, Assessment of the Effects of Extreme Heat Events on Buildings, (2021).
[196] J. Zou, A. Gaur, L. (Leon) Wang, A. Laouadi, M. Lacasse, Assessment of future overheating conditions in Canadian cities using a reference year selection method, Build. Environ. (2022). https://doi.org/10.1016/j.buildenv.2022.109102.
[197] J. Zou, H. Lu, C. Shu, L. Ji, A. Gaur, L. (Leon) Wang, Multiscale numerical assessment of urban overheating under climate projections: A review, Urban Clim. 49 (2023) 101551. https://doi.org/https://doi.org/10.1016/j.uclim.2023.101551.
[198] C.; Zhang, O.B. Kazanci, S. Attia, R. Levinson, S.H. Lee, P. Holzer, A. Salvatif, A. Machard, M. Pourabdollahtootkaboni, A. Gaur, B.W. Olesen, P. Heiselberg, General rights IEA EBC Annex 80-Dynamic simulation guideline for the performance testing of resilient cooling strategies, Citation. (2021).
[199] M. P.tootkaboni, I. Ballarini, M. Zinzi, V. Corrado, A comparative analysis of different future weather data for building energy performance simulation, Climate. (2021). https://doi.org/10.3390/cli9020037.
[200] M. Deru, K. Field, D. Studer, K. Benne, B. Griffith, P. Torcellini, B. Liu, M. Halverson, D. Winiarski, M. Rosenberg, M. Yazdanian, J. Huang, D. Crawley, U.S. Department of Energy commercial reference building models of the national building stock, Publ. (2011) 1–118. http://digitalscholarship.unlv.edu/renew_pubs/44.
[201] K. Menberg, Y. Heo, R. Choudhary, Sensitivity analysis methods for building energy models: Comparing computational costs and extractable information, Energy Build. (2016). https://doi.org/10.1016/j.enbuild.2016.10.005.
[202] T. Wei, A review of sensitivity analysis methods in building energy analysis, Renew. Sustain. Energy Rev. (2013). https://doi.org/10.1016/j.rser.2012.12.014.
[203] H. Lim, Z.J. Zhai, Comprehensive evaluation of the influence of meta-models on Bayesian calibration, Energy Build. (2017). https://doi.org/10.1016/j.enbuild.2017.09.009.
[204] E.W. Peterson, J.P. Hennessey, ON THE USE OF POWER LAWS FOR ESTIMATES OF WIND POWER POTENTIAL., J. Appl. Meteorol. (1978). https://doi.org/10.1175/1520-0450(1978)017<0390:OTUOPL>2.0.CO;2.
[205] S. Magli, C. Lodi, L. Lombroso, A. Muscio, S. Teggi, Analysis of the urban heat island effects on building energy consumption, Int. J. Energy Environ. Eng. (2015). https://doi.org/10.1007/s40095-014-0154-9.
[206] D. Hou, I.G. Hassan, L. Wang, Review on building energy model calibration by Bayesian inference, Renew. Sustain. Energy Rev. (2021). https://doi.org/10.1016/j.rser.2021.110930.