Journal of Urban Environmental Management

A Systematic Analysis of Urban Simulation Studies in Estimating Energy Demand: Lessons for Iran’s Four Climate Zones

Authors

Amin Amini kashani 1
reyhaneh Ashtiyani 2

1 PhD in Urban Planning,, Faculty of Architecture and Urban Planning, Tabriz Islamic Art University, Iran.

2 PhD in Urban Planning,, Faculty of Architecture and Urban Planning,, Iran University of Science and Technology, Iran

https://doi.org/10.48306/juem.2026.575734.1145
Abstract
The rapid growth of urban populations and energy consumption, coupled with the limitations of fossil fuel resources and environmental crises, has highlighted the necessity of managing energy use in cities. Urban energy simulation using semantic 3D models, due to cost reduction, time efficiency, and the ability to evaluate diverse scenarios, is an effective tool for predicting and optimizing energy consumption in existing urban fabrics as well as designing new neighborhoods and cities. This study reviews urban simulation research over the past decade in estimating energy demand. The findings indicate that semantic 3D models enable precise calculation of energy demand, consumption simulation, analysis of renewable energy potential, and assessment of building renovation scenarios. Optimal use of roof space, simplification of building components, and integration of spatial, energy, and climate data enhance simulation accuracy while reducing computation time. Considering the climatic diversity of Iran, it was found that energy simulation parameters must be adjusted according to each climate: in hot-dry and hot-humid regions, the focus should be on cooling load management and solar energy utilization; in cold and mountainous climates, reducing heating loads and improving building insulation are essential; and in temperate-humid climates, combined consumption patterns and humidity and heating–cooling control must be accounted for. Accordingly, developing semantic 3D city models, establishing building energy databases, generating urban energy maps, and performing dynamic energy simulations can serve as effective tools for energy management, efficiency enhancement, sustainable planning, and supporting strategic decision-making in Iran’s architecture and urban planning sectors.

Keywords

Urban simulation
modeling approaches
energy demand
Iran climate
1.خسروی کزازی، ع. و حسینعلی، ف. (1399). ارزیابی قابلیت داده های مکانی داوطلبانه در ایجاد منظر سه بعدی شهر. برنامه ریزی و آمایش فضا.
2.میروهابی ، س. و عباسپور، ر. (1395). استخراج مدل CityGML ساختمان‌ها در سطح 4 جزئیات از اطلاعات پایگاه داده داوطلبانه OSM. علوم و فنون نقشه برداری، دوره ششم، شماره 1، 185-198.
3.وکیلی نژاد، ر. (1398). بررسی ضرورت آموزش جامع شبیه سازی انرژی به دانشجویان کارشناسی ارشد معماری. فصلنامه آموزش مهندسی ایران، 79-100.
4. یکتا، م. و گل افشان ، س. (1394). بررسی انواع شیوه های نوین شبیه سازی انرژی در حوزه طراحی شهری. کنفرانس و نمایشگاه بین المللی رویکردهای نوین در نگهداشت انرژی.
5. Jang, Y.-H., Park, S. l., Kwon, T. H., & Lee, S.-H. (2021). CityGML urban model generation using national public datasets for flood damage simulations: A case study in Korea. Journal of Environmental Management.
6. Murshed, S. M., Picard, S., & Koch, A. (2018). Modelling, Validation and Quantification of Climate and Other Sensitivities of Building Energy Model on 3D City Models. ISPRS International Journal of Geo-Information.
7. Agugiaro, G. (2015). Energy planning tools and CityGML-based 3D virtual city models: experiences from Trento (Italy). Appl Geomat, 41-56.
8. Ang, Y. Q., Berzolla, Z. M., & Reinhart, C. F. (2020). From concept to application: A review of use cases in urban building energy modeling. Applied Energy.
9.Barnes , M., & Finch, E. L. (2008). COLLADA – Digital Asset Schema Release 1.5.0. Sony Computer Entertainment Inc.
10. Chen, Y., Hong, T., Luo, X., & Hooper, B. (2019). Development of city buildings dataset for urban building energy modeling. Energy and Buildings, 252-265.
11. Clarke, J. (2001). Energy simulation in building design. Butterworth-Heinemann.
12. Coors, V., & Zipf, A. (2007). MoNa 3D – mobile navigation using 3D city models. 4th International Symposium on LBS and Telecartography. Hong Kong.
13.De Jaeger, I., Reynders, G., Ma, Y., & Saelens, D. (2018). Impact of building geometry description within district energy simulations. Energy, 1060-1069.
14.  Egusquiza, A., Prieto, I., Izkara, J. L., & Béjar, R. (2018). Multi-scale urban data models for early-stage suitability assessment of energy conservation measures in historic urban areas. Energy & Buildings, 87-98.
15.Goetz, M., & Zipf , A. (2013). The Evolution of Geo-Crowdsourcing: Bringing Volunteered Geographic Information to the Third Dimension. In D. Sui, S. Elwood, & M. Goodchild, Crowdsourcing Geographic Knowledge (pp. 139-159). Springer, Dordrecht.
16.  Hopfe, C. J. (2009). Uncertainty and sensitivity analysis in building performance simulation for decision support and design optimization. PhD thesis, Technische Universiteit Eindhoven.
17. Kolbe, T. H. (2019). 3D City Database for CityGML. Chair of Geoinformatics, Technische Universität München.
18. Kolbe, T. H. (2009). Representing and Exchanging 3D City Models with CityGML. In J. Lee , & S. Zlatanova, Lecture Notes in Geoinformation and Cartography (pp. 15-31). Springer, Berlin, Heidelberg.
19. Kolbe, T. H., Gröger, G., & Plümer, L. (2008). CityGML – 3D city models and their potential for emergency response.
20.Malhotra, A., Shamovich, M., Frisch, J., & van Treeck, C. (2022). Urban energy simulations using open CityGML models: A comparative analysis. Energy & Buildings.
21.Metral, C., Falquet, G., & Karatzas, K. (2012). Ontologies for the Integration of Air Quality Models and 3D City Models. ArXiv.
22.Paiho, S., Ketomäki, J., Kannari, L., Häkkinen, T., & Shemeikka, J. (2019). A new procedure for assessing the energy-efficient refurbishment of buildings on district scale. Sustainable Cities and Society.
23.Rodríguez, L. R., Duminil, E., Ramos, J. S., & Eicker, U. (2017). Assessment of the photovoltaic potential at urban level based on 3D city models: A case study and new methodological approach. Solar Energy, 264-275.
24.Saran, S., Wate, P., Srivastav, S. K., & Murthy, K. (2015). CityGML at semantic level for urban energy conservation strategies. Annals of GIS, 27-41.
25.Shiode, N. (2000). 3D urban models: Recent developments in the digital modelling of urban environments in three-dimensions. GeoJournal, 263-269.
26.Strzalka, A., Alam, N., Duminil, E., Coors, V., & Eicker, U. (2012). Large scale integration of photovoltaics in cities. Applied Energy, 413-421.
27. Zhu, W., Simons, A., Wursthorn, S., & Nichersu, A. (2016). Integration of CityGML and Air Quality Spatio-Temporal Data Series via OGC SOS. Geospatial Sensor Webs Conference.
28.E. Kamel, A.M. Memari, Review of BIM’s application in energy simulation: Tools, issues, and solutions, Autom. Constr. 97 (2019) 164–180.
29.J.-S. Chou, D.-K. Bui, Modeling heating and cooling loads by artificial intelligence for energy-efficient building design, Energy Build. 82 (2014) 437–446.
30.S. Seyedzadeh, F.P. Rahimian, I. Glesk, M. Roper, Machine learning for estimation of building energy consumption and performance: A Review,Visualiz. Eng. 6 (1) (2018) 1–20.
31.D.R. Wulfinghoff, V. Garg, V. Garg, J. Mathur, Energy Conservation Building Code tip sheet, 2009.
32.Seyedzadeh, F.P. Rahimian, I. Glesk, M. Roper, Machine learning for estimation of building energy consumption and performance: A Review, Visualiz. Eng. 6 (1) (2018) 1 20.
33.J.-S. Chou, D.-K. Bui, Modeling heating and cooling loads by artificial intelligence for energy-efficient building design, Energy Build. 82 (2014) 437–446.
34.E. Kamel, A.M. Memari, Review of BIM’s application in energy simulation: Tools, issues, and solutions, Autom. Constr. 97 (2019) 164–180.
35.D.R. Wulfinghoff, V. Garg, V. Garg, J. Mathur, Energy Conservation Building Code tip sheet, 2009.
36.A. Tulsyan, S. Dhaka, J. Mathur, J.V. Yadav, Potential of energy savings through  implementation of Energy Conservation Building Code in Jaipur city, India, Energy Build. 58 (2013) 123–130.
37.D.G. Sanchez, B. Lacarrière, M. Musy, B. Bourges, Application of sensitivity analysis in building energy simulations: combining first-and second-order elementary effects methods, Energy Build. 68 (2014) 741–750.
38.R. Baetens, R. De Coninck, F. Jorissen, D. Picard, L. Helsen, D. Saelens, Openideas-an open framework for integrated district energy simulations, Build. Simul345–354 (2015).
39. D. Crawley, J. Hand, M. Kummert, B. Griffith, Contrasting the capabilities of building energy performance simulation programs, Build. Environ. 43 (2008) 661–673, https://doi.org/10.1016/j.buildenv.2006.10.027.
40.G. Gröger, T.H. Kolbe, C. Nagel, K.-H. Häfele, OGC City Geography Markup Language (CityGML) Encoding Standard, Version: 2.0.0, Tech. rep, Open Geospatial Consortium, OGC (2012) 12–019.
41..J. Benner, A. Geiger, G. Gröger, K.-H. Häfele, M. Löwner, Enhanced LoD concepts for virtual 3D city models, ISPRS Ann. Photogram., Remote Sens. Spatial Inform. Sci. 2 (2013) 51–61.
42.Sameer Saran, Parag Wate, S.K. Srivastav & Y.V.N. Krishna Murthy (2015) CityGML at semantic level for urban energy conservation strategies, Annals of GIS, 21:1, 27-41, DOI: 10.1080/19475683.2014.992370
43.N. Nurhasanah, The Effort of Germany on Addressing Climate Change Through Climate Action Plan 2050, Ph.D. thesis, University of Muhammadiyah Malang (2019).
44.J. Huang, Exploring Climate Framework Laws and The Future of Climate Action, Pace Environmental Law Review 38 (2) (2021) 285.
45.R. Kinley, M.Z. Cutajar, Y. de Boer, C. Figueres, Beyond good intentions, to urgent action: Former UNFCCC leaders take stock of thirty years of international climate change negotiations, Climate Policy 21 (5) (2021) 593–603.
46.S. Oberthür, C. Dupont, The European Union’s international climate leadership: towards a grand climate strategy?, J Eur. Public Policy (2021) 1–20.
47. Institute for Housing and Environment, TABULA WebTool (2017). URL: https://webtool.building-typology.eu/#bm.
48. Remondino F, Poli D (2014) Back to the future: Il ritorno della fotogrammetria. GEOmedia 2014(2):6–8
Journal of Urban Environmental Management
Volume 4, Issue 1 - Serial Number 13
Winter 2026
Pages 106-132

  • Receive Date 14 February 2026
  • Revise Date 22 February 2026
  • Accept Date 06 May 2026
  • First Publish Date 06 May 2026
  • Publish Date 21 March 2026

Home

Guide for Authors

Submit Manuscript

Reviewers

Contact Us