Early-Stage Building Performance Simulation for High-Performance Buildings: A Systematic Review of Methods, Workflows, Uncertainty Handling, and Design Decision Support
DOI:
https://doi.org/10.15377/2409-9821.2026.13.8Keywords:
Collaborative workflow, Design decision support, Early-stage architectural design, Uncertainty and sensitivity analysis, Building performance simulation (BPS).Abstract
This systematic review of the literature examines how early-stage building performance evaluation can assist designers while they are still able to make alterations to their designs. The literature covered in this review comprised all peer-reviewed publications in English from 2013-2025. The review has synthesized the literature into four separate themes: (1) Performance Evaluation and Performance Assessment; (2) Simulation-based Design; (3) Collaborative and Sustainable Workflow Processes; and (4) Early-Stage Simulation Practices. The results show that the evaluation approach now emphasizes performance early in the design process rather than compliance at the final stage. Initial simulations target the building's massing and envelop in relation to climate considerations; later stages will focus on technical enhancements, verification, and ensuring adherence to building codes. The effectiveness of these procedures depends on the ability to establish decision-phase milestones, integrate design tools with performance analysis software, and openly discuss uncertainties throughout the design journey. Effective early-stage performance evaluation is restricted by a number of factors, including inconsistent conversion of Building Information Models (BIMs) to Building Energy Models (BEMs); a lack of verification/validation techniques; and several situations in which the building's actual performance differs from its anticipated energy performance. To improve the integration of performance evaluation into architectural design practice, these obstacles necessitate significant improvements in interoperability, systematic uncertainty reporting, and workflow-oriented methodologies for buildings and building codes.
Downloads
References
[1] De Wilde P. Building performance analysis. John Wiley & Sons; 2018. https://doi.org/10.1002/9781119341901
[2] Hong T, Langevin J, Sun K, Eds. Building simulation: ten challenges. Build Simul. 2018; 11: 871-98. https://doi.org/10.1007/s12273-018-0444-x
[3] Xie X, Gou Z. Building performance simulation as an early intervention or late verification in architectural design: same performance outcome but different design solutions. J Green Build. 2017; 12(1): 45-61. https://doi.org/10.3992/1552-6100.12.1.45
[4] Attia S, Gratia E, De Herde A, Hensen J. Early decision support for net zero energy buildings design using building performance simulation. In: Proceedings of the International Conference on Cleantech for Smart Cities and Buildings (CISBAT 2013); 2013 Sep 4-6; Lausanne, Switzerland. p. 921-6.
[5] Negendahl K. Building performance simulation in the early design stage: an introduction to integrated dynamic models. Autom Constr. 2015; 54: 39-53. https://doi.org/10.1016/j.autcon.2015.03.002
[6] Azari R, Kim Y-W. Integration evaluation framework for integrated design teams of green buildings: development and validation. J Manage Eng. 2016; 32(3): 04015053. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000416
[7] Raouf AM, Al-Ghamdi SG. Framework to evaluate quality performance of green building delivery: project brief and design stage. Buildings. 2021; 11(10): 473. https://doi.org/10.3390/buildings11100473
[8] Feng X, Yan D, Hong T. Simulation of occupancy in buildings. Energy Build. 2015; 87: 348-59. https://doi.org/10.1016/j.enbuild.2014.11.067
[9] Hong T, Li C, Diamond R, Yan D, Zhang Q, Zhou X, et al. Integrated design for high performance buildings. Berkeley (CA): Lawrence Berkeley National Laboratory; 2014 Jul. Report No.: LBNL-6991E.
[10] Østergård T, Jensen RL, Maagaard SE. Building simulations supporting decision making in early design-a review. Renew Sustain Energy Rev. 2016; 61: 187-201. https://doi.org/10.1016/j.rser.2016.03.045
[11] Hopfe CJ, Hensen JLM. Uncertainty analysis in building performance simulation for design support. Energy Build. 2011; 43(10): 2798-805. https://doi.org/10.1016/j.enbuild.2011.06.034
[12] Pati D, Lorusso LN. How to write a systematic review of the literature. HERD. 2018; 11(1): 15-30. https://doi.org/10.1177/1937586717747384
[13] Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. BMJ. 2009; 339: b2535. https://doi.org/10.1136/bmj.b2535
[14] Aksamija A. A strategy for energy performance analysis at the early design stage: predicted vs actual building energy performance. J Green Build. 2015; 10(3): 161-76. https://doi.org/10.3992/jgb.10.3.161
[15] Ali A, Jayaraman R, Azar E, Maalouf M. A comparative analysis of machine learning and statistical methods for evaluating building performance: a systematic review and future benchmarking framework. Build Environ. 2024; 252: 111268. https://doi.org/10.1016/j.buildenv.2024.111268
[16] An N, Li X, Yang H, Pang X, Gao G, Ding D. From building information modeling to building energy modeling: optimization study for efficient transformation. Buildings. 2024; 14(8): 2444. https://doi.org/10.3390/buildings14082444
[17] Asl MR, Stoupine A, Zarrinmehr S, Yan W. Optimo: a BIM-based multi-objective optimization tool utilizing visual programming for high performance building design. In: Proceedings of the 33rd eCAADe Conference; 2015; Vienna, Austria. p. 673-82. https://doi.org/10.52842/conf.ecaade.2015.1.673
[18] Ashuri B, Wang J, Shahandashti M, Baek M. A data envelopment analysis (DEA) model for building energy benchmarking. J Eng Des Technol. 2019; 17(4): 747-68. https://doi.org/10.1108/JEDT-08-2018-0127
[19] Augenbroe G. The role of simulation in performance-based building. In: Building performance simulation for design and operation. 2nd ed. Routledge; 2019. p. 343-73. https://doi.org/10.1201/9780429402296-10
[20] Beetge WG, De Canha D, Pretorius J. Managing the design and development of high-performance buildings through integrated design. In: IEEE Innovative Smart Grid Technologies-Asia (ISGT-Asia). IEEE; 2017. p. 1-6. https://doi.org/10.1109/ISGT-Asia.2017.8378400
[21] Berawi MA, Kim AA, Naomi F, Basten V, Miraj P, Medal LA, et al. Designing a smart integrated workspace to improve building energy efficiency: an Indonesian case study. Int J Constr Manage. 2023; 23(3): 410-22. https://doi.org/10.1080/15623599.2021.1882747
[22] Bjørnskov J, Jradi M, Wetter M. Automated model generation and parameter estimation of building energy models using an ontology-based framework. Energy Build. 2025; 329: 115228. https://doi.org/10.1016/j.enbuild.2024.115228
[23] Botchway EA, Agyekum K, Kotei-Martin JN, Afram SO. Utilization of simulation tools for building performance assessment among design professionals. Int J Build Pathol Adapt. 2025; 43(5): 1140-60. https://doi.org/10.1108/IJBPA-01-2023-0006
[24] Brown C, Rajkovich N, Gilman E, LaRue A, Keast J, Eds. The future of weather files for building performance simulation in New York State. In: Proceedings of Building Simulation 2023: 18th Conference of IBPSA; 2023; Shanghai, China. p. 1748-55. https://doi.org/10.26868/25222708.2023.1384
[25] Christine Sotsek N, Sanchez Leitner D, Lacerda Santos AdP. A systematic review of Building Performance Evaluation criterias (BPE). Rev ALCONPAT. 2019; 9(1): 1-14. https://doi.org/10.21041/ra.v9i1.260
[26] De Wilde P. The gap between predicted and measured energy performance of buildings: a framework for investigation. Autom Constr. 2014; 41: 40-9. https://doi.org/10.1016/j.autcon.2014.02.009
[27] Delgarm N, Sajadi B, Azarbad K, Delgarm S. Sensitivity analysis of building energy performance: a simulation-based approach using OFAT and variance-based sensitivity analysis methods. J Build Eng. 2018; 15: 181-93. https://doi.org/10.1016/j.jobe.2017.11.020
[28] Gerber DJ, Lin S-HE. Designing in complexity: simulation, integration, and multidisciplinary design optimization for architecture. Simulation. 2014; 90(8): 936-59. https://doi.org/10.1177/0037549713482027
[29] Han JM. A new interoperability framework for data-driven building performance simulation [doctoral dissertation]. Cambridge (MA): Harvard Graduate School of Design, Harvard University; 2022.
[30] Han T, Huang Q, Zhang A, Zhang Q. Simulation-based decision support tools in the early design stages of a green building-a review. Sustainability. 2018; 10(10): 3696. https://doi.org/10.3390/su10103696
[31] Hasan A, Palonen M, Eds. Simulation-based optimization for energy and buildings. In: Renewable energy in the service of mankind. Vol I. Selected topics from the World Renewable Energy Congress WREC 2014; 2015 Sep 10; Cham: Springer International Publishing. p. 503-13. https://doi.org/10.1007/978-3-319-17777-9_45
[32] Heidarinejad M, Dahlhausen M, McMahon S, Pyke C, Srebric J. Building classification based on simulated annual results: towards realistic building performance expectations. In: Proceedings of BS2013: 13th Conference of International Building Performance Simulation Association; 2013 Aug 26-28; Chambéry, France. p. 1706-13. https://doi.org/10.26868/25222708.2013.2516
[33] Hemsath TL. Conceptual energy modeling for architecture, planning and design: impact of using building performance simulation in early design stages. In: Proceedings of BS2013: 13th Conference of International Building Performance Simulation Association; 2013 Aug 26-28; Chambéry, France. p. 376-84. https://doi.org/10.26868/25222708.2013.2015
[34] Hensen JLM, Lamberts R. Building performance simulation-challenges and opportunities. 2nd ed. Routledge; 2019. p. 1-10. https://doi.org/10.1201/9780429402296-1
[35] Hu M, Ed. Optimized renovation strategies of education building-a novel BIM/BPM/BEM framework. In: Proceedings of Building Simulation 2019: 16th Conference of IBPSA; 2019; Rome, Italy. p. 106-12. https://doi.org/10.26868/25222708.2019.210333
[36] Isley CG. Examining integrated design workflows that support building performance integration in small architectural firms [doctoral dissertation]. North Carolina State University; 2021.
[37] Hopfe CJ, Soebarto V, Crawley D, Rawal R, Eds. Understanding the differences of integrating building performance simulation in the architectural education system. In: Proceedings of Building Simulation 2017: 15th Conference of IBPSA; 2017; San Francisco, CA, USA. p. 1249-56. https://doi.org/10.26868/25222708.2017.319
[38] Jaganathan S, Mohammed AH, Rahman MSA. Descriptive review of energy performance evaluation approaches. Sains Humanika. 2016; 8(4-3): 59-63. https://doi.org/10.11113/sh.v8n4-3.1082
[39] Jia M, Srinivasan R, Ries RJ, Bharathy G, Weyer N. Investigating the impact of actual and modeled occupant behavior information input to building performance simulation. Buildings. 2021; 11(1): 32. https://doi.org/10.3390/buildings11010032
[40] Kamari A, Corrao R, Petersen S, Kirkegaard PH, Eds. Sustainable renovation framework: introducing three levels of integrated design process implementation and evaluation. In: Proceedings of the 33rd PLEA International Conference: Design to Thrive; 2017 Jul 2-5; Edinburgh, UK. p. 748-55.
[41] Kono J, McNulty MK, Abramson B. Raising the bar: comparing building energy benchmarking methods. ASHRAE Trans. 2020; 126(1): 300-7.
[42] Krstić H, Teni M, Eds. Review of methods for buildings energy performance modelling. IOP Conf Ser Mater Sci Eng. 2017. https://doi.org/10.1088/1757-899X/245/4/042049
[43] Lamberts R, Hensen JLM. Building performance simulation for design and operation. Spoon Press; 2011.
[44] Lee J. The integrated design process from the facilitator's perspective. Int J Art Des Educ. 2014; 33(1): 141-56. https://doi.org/10.1111/j.1476-8070.2014.12000.x
[45] Li J, Iulo LD, Poerschke U. A review of the energy performance gap between predicted and actual use in buildings. Build Simul Conf Proc. 2023; 18: 3406-13. https://doi.org/10.26868/25222708.2023.1430
[46] Li H, Hong T, Lee SH, Sofos M. System-level key performance indicators for building performance evaluation. Energy Build. 2020; 209: 109703. https://doi.org/10.1016/j.enbuild.2019.109703
[47] Li S, Liu L, Peng C. A review of performance-oriented architectural design and optimization in the context of sustainability: dividends and challenges. Sustainability. 2020; 12(4): 1427. https://doi.org/10.3390/su12041427
[48] Lin S-H, Gerber DJ. Evolutionary energy performance feedback for design: multidisciplinary design optimization and performance boundaries for design decision support. Energy Build. 2014; 84: 426-41. https://doi.org/10.1016/j.enbuild.2014.08.034
[49] Lin B, Chen H, Yu Q, Zhou X, Lv S, He Q, et al. MOOSAS-a systematic solution for multiple objective building performance optimization in the early design stage. Build Environ. 2021; 200: 107929. https://doi.org/10.1016/j.buildenv.2021.107929
[50] Lu Y, Sood T, Chang R, Liao L. Factors impacting integrated design process of net zero energy buildings: an integrated framework. Int J Constr Manage. 2022; 22(9): 1700-12. https://doi.org/10.1080/15623599.2020.1742625
[51] Mahmoud R, Kamara JM, Burford N. Opportunities and limitations of building energy performance simulation tools in the early stages of building design in the UK. Sustainability. 2020; 12(22): 9702. https://doi.org/10.3390/su12229702
[52] Nazeer FS, Kamardeen I, Hasan A. Research needs for enhancing pre-occupancy evaluation of buildings. Built Environ Proj Asset Manag. 2024; 14(4): 529-46. https://doi.org/10.1108/BEPAM-11-2023-0190
[53] Nguyen A-T, Reiter S, Rigo P. A review on simulation-based optimization methods applied to building performance analysis. Appl Energy. 2014; 113: 1043-58. https://doi.org/10.1016/j.apenergy.2013.08.061
[54] O'Brien W, Tahmasebi F, Andersen RK, Azar E, Barthelmes V, Belafi ZD, et al. An international review of occupant-related aspects of building energy codes and standards. Build Environ. 2020; 179: 106906. https://doi.org/10.1016/j.buildenv.2020.106906
[55] Pan Y, Zhu M, Lv Y, Yang Y, Liang Y, Yin R, et al. Building energy simulation and its application for building performance optimization: a review of methods, tools, and case studies. Adv Appl Energy. 2023; 10: 100135. https://doi.org/10.1016/j.adapen.2023.100135
[56] Purup PB, Petersen S. Research framework for development of building performance simulation tools for early design stages. Autom Constr. 2020; 109: 102966. https://doi.org/10.1016/j.autcon.2019.102966
[57] Rahman Azari PC, Kim Y-W. Evaluating integrated design process of high-performance green buildings. In: 49th ASC Annual International Conference Proceedings; 2013 Apr 10-13; San Luis Obispo, CA, USA.
[58] Rezaee R, Brown J, Augenbroe G. Building energy performance estimation in early design decisions: quantification of uncertainty and assessment of confidence. In: Construction Research Congress 2014: Construction in a Global Network; 2014 May 19-21; Atlanta, GA, USA. p. 2195-204. https://doi.org/10.1061/9780784413517.223
[59] Ribeiro D. Developments in local energy efficiency policy: a review of recent progress and research. Curr Sustain Renew Energy Rep. 2018; 5: 109-15. https://doi.org/10.1007/s40518-018-0105-9
[60] Roger Chang PE, B, Crawley DB. A metric to characterize commercial building process loads, energy use. ASHRAE J. 2019;61(11):12-22.
[61] Rosenberg M, Eley C. A stable whole building performance method for Standard 90.1, part 2. ASHRAE J. 2016;58(6):28-42.
[62] Sayın S, Çelebi G. A practical approach to performance-based building design in architectural project. Build Res Inf. 2020; 48(4): 446-68. https://doi.org/10.1080/09613218.2019.1669008
[63] Shen X, Singhvi A, Mengual A, Spastri M, Watson V. Evaluating the multi-objective optimization methodology for performance-based building design in professional practice. In: 2018 Building Performance Analysis Conference and SimBuild co-organized by ASHRAE and IBPSA-USA; 2018; Chicago, IL. p. 646-53.
[64] Soebarto V, Hopfe C, Crawley D, Rawal R, Eds. Capturing the views of architects about building performance simulation to be used during design processes. In: Proceedings of the BS2015: 14th Conference of International Building Performance Simulation Association; 2015; Hyderabad, India. https://doi.org/10.26868/25222708.2015.2790
[65] Sohn MD, Dunn LN. Exploratory analysis of energy use across building types and geographic regions in the United States. Front Built Environ. 2019; 5: 105. https://doi.org/10.3389/fbuil.2019.00105
[66] Stevenson F. Embedding building performance evaluation in UK architectural practice and beyond. Build Res Inf. 2019; 47(3): 305-17. https://doi.org/10.1080/09613218.2018.1467542
[67] Stavrakantonaki M. A framework for input data processing during building energy model calibration, a case study. In: Proceedings of the 33rd eCAADe Conference; 2015 Sep 16-18; Vienna, Austria. p. 625-32. https://doi.org/10.52842/conf.ecaade.2015.1.625
[68] Taylor J, Liu Y, Lin B, Burman E, Hong S-M, Yu J, et al. Towards a framework to evaluate the ‘total’ performance of buildings. Build Serv Eng Res Technol. 2018; 39(5): 609-31. https://doi.org/10.1177/0143624418762662
[69] Terim Cavka B, Cavka HB, Salehi MM. An investigation of the design process's effect on a high-performance building's actual energy system performance. J Integr Des Process Sci. 2023; 26(1): 85-100. https://doi.org/10.3233/JID-220002
[70] Tian W, Heo Y, De Wilde P, Li Z, Yan D, Park CS, et al. A review of uncertainty analysis in building energy assessment. Renew Sustain Energy Rev. 2018; 93: 285-301. https://doi.org/10.1016/j.rser.2018.05.029
[71] Tian Z, Zhang X, Wei S, Du S, Shi X. A review of data-driven building performance analysis and design on big on-site building performance data. J Build Eng. 2021; 41: 102706. https://doi.org/10.1016/j.jobe.2021.102706
[72] Utkucu D, Sözer H. Interoperability and data exchange within BIM platform to evaluate building energy performance and indoor comfort. Autom Constr. 2020; 116: 103225. https://doi.org/10.1016/j.autcon.2020.103225
[73] Van Dronkelaar C, Dowson M, Burman E, Spataru C, Mumovic D. A review of the energy performance gap and its underlying causes in non-domestic buildings. Front Mech Eng. 2016; 1: 17. https://doi.org/10.3389/fmech.2015.00017
[74] Vojdani B, Rahbar M, Fazeli M, Hakimazari M, Samuelson HW. Comparative study of optimization methods for building energy consumption and daylighting performance. Energy Build. 2024; 323: 114753. https://doi.org/10.1016/j.enbuild.2024.114753
[75] Wang L. Workflow for applying optimization-based design exploration to early-stage architectural design-case study based on EvoMass. Int J Archit Comput. 2022; 20(1): 41-60. https://doi.org/10.1177/14780771221082254
[76] Wang L, Janssen P, Bui TDP, Chen KW. Comparing design strategies: a system for optimization-based design exploration. In: 28th International Conference on Computer-Aided Architectural Design Research in Asia, CAADRIA 2023. The Association for Computer-Aided Architectural Design Research in Asia; 2023. p. 221-30. https://doi.org/10.52842/conf.caadria.2023.1.221
[77] Webb AL, McConnell C. Evaluating the feasibility of achieving building performance standards targets. Energy Build. 2023; 288: 112989. https://doi.org/10.1016/j.enbuild.2023.112989
[78] Wortmann T, Cichocka J, Waibel C. Simulation-based optimization in architecture and building engineering-results from an international user survey in practice and research. Energy Build. 2022; 259: 111863. https://doi.org/10.1016/j.enbuild.2022.111863
[79] Yigit S, Ozorhon B. A simulation-based optimization method for designing energy efficient buildings. Energy Build. 2018; 178: 216-27. https://doi.org/10.1016/j.enbuild.2018.08.045
[80] Yin R, Liu J, Piette MA, Xie J, Pritoni M, Casillas A, et al. Comparing simulated demand flexibility against actual performance in commercial office buildings. Build Environ. 2023; 243: 110663. https://doi.org/10.1016/j.buildenv.2023.110663
[81] Agarwal M, Pastore L, Andersen M. Risk of incorrect choices due to uncertainty in BPS evaluations of conceptual-stage neighbourhood-scale building designs. J Build Perform Simul. 2024; 17(2): 234-52. https://doi.org/10.1080/19401493.2023.2253458
[82] Alsaadani S, Bleil De Souza C. Performer, consumer or expert? A critical review of building performance simulation training paradigms for building design decision-making. J Build Perform Simul. 2019; 12(3): 289-307. https://doi.org/10.1080/19401493.2018.1447602
[83] İşeri OK, Dursun O. The impacts of early architectural design decisions on building performance. Int J Digit Innov Built Environ. 2022; 11(2): 1-21. https://doi.org/10.4018/IJDIBE.301245
[84] Asl MR, Zarrinmehr S, Bergin M, Yan W. BPOpt: a framework for BIM-based performance optimization. Energy Build. 2015; 108: 401-12. https://doi.org/10.1016/j.enbuild.2015.09.011
[85] Attia S, Bilir S, Safy T, Struck C, Loonen R, Goia F. Current trends and future challenges in the performance assessment of adaptive façade systems. Energy Build. 2018; 179: 165-82. https://doi.org/10.1016/j.enbuild.2018.09.017
[86] Azhar S. Building information modeling (BIM): trends, benefits, risks, and challenges for the AEC industry. Leadersh Manage Eng. 2011; 11(3): 241-52. https://doi.org/10.1061/(ASCE)LM.1943-5630.0000127
[87] Hensen JLM, Lamberts R. Building performance simulation for design and operation. London: Routledge; 2012, pp. 1-536. https://doi.org/10.4324/9780203891612
[88] Li Z, Tian M, Zhu X, Xie S, He X. A review of integrated design process for building climate responsiveness. Energies. 2022; 15(19): 7133. https://doi.org/10.3390/en15197133
[89] Donn M. Simulation and the building performance gap. Build Cities. 2025; 6(1): 713-28. https://doi.org/10.5334/bc.688
[90] Arjunan P, Poolla K, Miller C. EnergyStar++: towards more accurate and explanatory building energy benchmarking. Appl Energy. 2020; 276: 115413. https://doi.org/10.1016/j.apenergy.2020.115413
[91] Jia M, Srinivasan R. Building performance evaluation using coupled simulation of EnergyPlus™ and an occupant behavior model. Sustainability. 2020; 12(10): 4086. https://doi.org/10.3390/su12104086
[92] Ahmed O, Sezer N, Ouf M, Wang LL, Hassan IG. State-of-the-art review of occupant behavior modeling and implementation in building performance simulation. Renew Sustain Energy Rev. 2023; 185: 113558. https://doi.org/10.1016/j.rser.2023.113558
[93] Bahadori-Jahromi A, Salem R, Mylona A, Hasan AU, Zhang H. The effect of occupants' behaviour on the building performance gap: UK residential case studies. Sustainability. 2022; 14(3): 1362. https://doi.org/10.3390/su14031362
[94] Passe U. A design workflow for integrating performance into architectural education. Build Cities. 2020; 1(1): 565-78. https://doi.org/10.5334/bc.48
[95] Rosenberg M, Eley C, PE F. A stable whole building performance method for Standard 90.1. ASHRAE J. 2013; 55(5): 33.
[96] Cloudt CL, Gomez JD, Nishimoto TK, Shephard LE, Eds. Coupling simulation tools and real-time data to improve building energy performance. In: 2013 IEEE Green Technologies Conference (GreenTech); 2013. IEEE. https://doi.org/10.1109/GreenTech.2013.64
[97] Hu M. Optimal renovation strategies for education buildings-a novel BIM-BPM-BEM framework. Sustainability. 2018; 10(9): 3287. https://doi.org/10.3390/su10093287
[98] Asdrubali F, Manzo M, Grazieschi G, Eds. Interoperability between BIM and building energy modelling-a case study. In: Building Simulation 2021; 2021. IBPSA. https://doi.org/10.26868/25222708.2021.30849
[99] Ciccozzi A, de Rubeis T, Paoletti D, Ambrosini D. BIM to BEM for building energy analysis: a review of interoperability strategies. Energies. 2023; 16(23): 7845. https://doi.org/10.3390/en16237845
[100] Elnabawi MH. Building information modeling-based building energy modeling: investigation of interoperability and simulation results. Front Built Environ. 2020; 6: 573971. https://doi.org/10.3389/fbuil.2020.573971
[101] Nouri A, van Treeck C, Frisch J. Sensitivity assessment of building energy performance simulations using MARS meta-modeling in combination with Sobol' method. Energies. 2024; 17(3): 695. https://doi.org/10.3390/en17030695
[102] Menberg K, Heo Y, Choudhary R. Sensitivity analysis methods for building energy models: comparing computational costs and extractable information. Energy Build. 2016; 133: 433-45. https://doi.org/10.1016/j.enbuild.2016.10.005
Downloads
Published
License
Copyright (c) 2026 Sepideh Niknia, Hazem Rashed-Ali

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
All the published articles are licensed under the terms of the Creative Commons Attribution Non-Commercial License (CC BY-NC 4.0) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.
