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Research Article Journal of Energy Management and Technology (JEMT) Vol. 5, Issue 2 70Fig. 9. Separation of building blocks for energy analysis.Block A %u2192%u00125.4 %u2212 1313 %u0013%u2217 100 = %u221258.46% (1)Block A %u2192%u001286.7 %u2212 112112 %u0013%u2217 100 = %u221222.59% (2)Table 5. Comparison of different energy consumption scenariosin the building blocksBuilding BlockEnergy Cost Saving Energy Use Intensity SavingUSD/m2/year Percent kWh/m2/year PercentA BIM Parameters 13 0 112 0Optimized Parameters 5.4 58.46 86.7 22.59B BIM Parameters 13 0 119 0Optimized Parameters 6.47 50.23 99 16.81C BIM Parameters 13.6 0 126 0Optimized Parameters 6.66 51.03 105 16.67D BIM Parameters 14.1 0 119 0Optimized Parameters 5.89 58.23 99.6 16.3Middle lobby BIM Parameters 14.1 0 191 0Optimized Parameters 8.03 43.05 170 115. RESEARCH LIMITATIONSDue to the software limitation in sending the shade surfaces(max. 10000 surfaces) as well as the number of doors (max. 4096doors), the whole building energy analysis was not possible inthe cloud. For this reason, as shown in Fig. 9, each block ofthis residential building is analyzed separately. Finally, due toincreased shade surfaces, the ceiling elements were removedfrom the building model.6. DISCUSSIONAs mentioned, due to the software limitations in sending theenergy model, the building blocks were separated from eachother and were removed the ceiling elements from the buildingmodel. Therefore, the thermal height was 4 m on the first floorand 3.7 m on the other floors. This building could have lowerenergy consumption than the obtained values, due to the implementation of ceiling elements during the construction phase andreduced computational height of the spaces as a result. However,the results show that block A has the lowest energy consumption. Considering the similar materials and equipment used, thiscan be due to the building orientation towards the geographical north of the region. Accordingly, by the implementationof other blocks in the direction of block A, the lowest energyconsumption can be achieved as a result of the maximum solarradiation during a day. This study shows that the results fromthe conceptual model analysis are acceptable compared to theresults from the actual building model. It can be useful in theearly stages of decision-making for the project.7. CONCLUSIONToday, most of the environmental problems in the world arerelated to the use of fossil fuels, especially in the constructionsector. In Iran, considerable amounts of energy are consumedannually in the building and housing sectors. In this study, after reviewing the conceptual masses and choosing a buildingform, an exact model of building elements was created in theAutodesk Revit software. Then, the energy model was generatedbased on the BIM parameters. Finally, adjusting the parametersaffecting energy consumption led to the reduction of energycosts in the building. Finally, the results of parametric studies onalternative schemes of cost optimization showed that 58.46% ofenergy cost savings would be achieved compared to the initialmodel of the building on a 30-year time horizon. The resultsshowed that optimizing the energy consumption of the buildingusing building information modeling technology could significantly save energy costs. In this regard, the optimization ofenergy consumption would reduce environmental pollutantsemissions and contribute to the preservation and sustainabilityof the environment. It should be noted that the general methodand findings of this study can be used in all regions of the world.REFERENCES1. U.S. Energy Information Administration, %u201cAnnual Energy Outlook 2020,%u201dTechnical Report, 2020. https://www.eia.gov/outlooks/aeo/. (accessedJan. 29, 2020).2. Energy Efficiency & Renewable Energy, %u201cEmerging Technologies,%u201d2020. https://www.energy.gov/eere/buildings/emerging-technologies.(accessed Sep. 02, 2020).3. N. Amani, %u201cEnergy simulation and management of the main buildingcomponent materials using comparative analysis in amild climate zone,%u201dJournal of Renewable Energy and Environment, vol. 7, pp. 29-47, 2020.4. C. D. Douglass, %u201cInstructional modules demonstrating buildingenergy analysis using a building information model,%u201d Unpublished master%u2019s thesis. University of Illinois, Urbana-Champaign.https://www.ideals.illinois.edu/bitstream/handle/2142/18219/Douglass_Christian.pdf?sequence=1, 2010.5. N. Amani, %u201cBuilding energy conservation in atrium spaces based onECOTECT simulation software in hot summer and cold winter zone inIran,%u201d International Journal of Energy Sector Management, vol. 12, pp.298-313, 2018.6. J. Choi, J. Shin, M. Kim, and I. Kim, %u201cDevelopment of openBIM-basedenergy analysis software to improve the interoperability of energy performance assessment,%u201d Automation in Construction, vol. 72, pp. 52%u201364,2016.7. J. Park, J. Park, J. Kim, and J. Kim, %u201cBuilding information modellingbased energy performance assessment system: An assessment of theEnergy Performance Index in Korea,%u201d Construction Innovation, vol. 12,no. 3, pp. 335%u2013354, 2012.8. N. Amani, and E. Kiaee, %u201cDeveloping a two-criteria framework to rankthermal insulation materials in nearly zero energy buildings using multiobjective optimization approach,%u201d Journal of Cleaner Production, vol.276, pp. 122592, 2020.9. S. J. Guo and T. Wei, %u201cCost-effective energy saving measures basedon BIM technology: Case study at National Taiwan University,%u201d Energyand Buildings, vol. 127, pp. 433%u2013441, 2016.10. A. Schlueter and F. Thesseling, %u201cBuilding information model basedenergy/exergy performance assessment in early design stages,%u201d Automation in Construction, vol. 18, no. 2, pp. 153%u2013163, 2009.