Global publication trends and hotspots of building energy simulation based on bibliometric analysis: 1982-2022

[1] S.S. Abolhassani, M. Amayri, N. Bouguila, and U. Eicker, A new workflow for detailed urban-scale building energy modeling using spatial joining of attributes for archetype selection, J. Build. Eng. 46 (2022), 103661.

[2] A. Ahadi, A. Singh, M. Bower, and M. Garrett, Text mining in education: A bibliometrics-based systematic review, Educ. Sci. 12 (2022), no. 3, 210.

[3] J. Allegrini, V. Dorer, and J. Carmeliet, Coupled CFD, radiation and building energy model for studying heat fluxes in an urban environment with generic building configurations, Sustain. Cities Soc. 19 (2015), 385–394.

[4] M. Alnajem, M.M. Mostafa, and A.R. ElMelegy, Mapping the first decade of circular economy research: a bibliometric network analysis, J. Ind. Prod. Eng. 38 (2021), no. 1, 29–50.  

[5] M. Almatared, H. Liu, S. Tang, M. Sulaiman, Z. Lei, and H. X. Li, Digital twin in the architecture, engineering, and construction industry: A bibliometric review, Construction Research Congress 2022, Arlington, Virginia: American Society of Civil Engineers, 2022, pp. 670–678.

[6] M. Alwetaishi, Can we learn from heritage buildings to achieve nearly zero energy building and thermal comfort? A case study in a hot climate, Adv. Build. Energy Res. 16 (2020), 214–230.

[7] R. Andersen, V. Fabi, J. Toftum, S.P. Corgnati, and B.W. Olesen, Window opening behavior modeled from measurements in Danish dwellings, Build. Environ. 69 (2013), 101–113.

[8] A. Angelotti, L. Mazzarella, C. Cornaro, F. Frasca, A. Prada, P. Baggio, I. Ballarini, G. De Luca, and V. Corrado, Calibrating the dynamic energy simulation model for an existing building: Lessons learned from a collective exercise, Energies 16 (2023), no. 7, 2979.

[9] F. Ascione, L. Bellia, and F. Minichiello, Earth-to-air heat exchangers for Italian climates, Renew. Energy 36 (2011), no. 8, 2177–2188.

[10] F. Ascione, N. Bianco, C.D. Stasio, G.M. Mauro, and G.P. Vanoli, Artificial neural networks to predict energy performance and retrofit scenarios for any member of a building category: A novel approach, Energy 118 (2017), 999–1017.

[11] F. Ascione, N. Bianco, R. De Masi, G. Mauro, and G. Vanoli, Design of the building envelope: A novel multiobjective approach for the optimization of energy performance and thermal comfort, Sustainability 7 (2015), no. 8, 10809–10836.

[12] F. Ascione, N. Bianco, R.F. De Masi, F. De’ Rossi, and G.P. Vanoli, Energy refurbishment of existing buildings through the use of phase change materials: Energy savings and indoor comfort in the cooling season, Appl. Energy 113 (2014), 990–1007.

[13] F. Ascione, N. Bianco, F. de’ Rossi, G. Turni, and G.P. Vanoli, Green roofs in European climates. Are effective solutions for the energy savings in air-conditioning?, Appl. Energy 104 (2013), 845-848.

[14] F. Ascione, N. Bianco, C.D. Stasio, G.M. Mauro, and G.P. Vanoli, Multi-stage and multi-objective optimization for energy retrofitting a developed hospital reference building: A new approach to assess cost-optimality, Appl. Energy 174 (2016), 37–68.

[15] F. Ascione, N. Bianco, C.D. Stasio, G.M. Mauro, and G.P. Vanoli, Simulation-based model predictive control by the multi-objective optimization of building energy performance and thermal comfort, Energy Build. 111 (2016), 131–144.

[16] S. Attia, Building Performance Simulation Tools: Selection Criteria and User Survey, Universit Catholique de Louvain: Louvain La Neuve, Belgium, 2010.

[17] S. Attia, State of the Art of Existing Early Design Simulation Tools for Net Zero Energy Buildings: A Comparison of Ten Tools, Architecture et climat, Louvain La Neuve, Belgium, 2011.

[18] E. Azar and C.C. Menassa, A comprehensive analysis of the impact of occupancy parameters in energy simulation of office buildings, Energy Build. 55 (2012), 841–853.

[19] A. Babalola, S. Musa, M. T. Akinlolu, and T.C. Haupt, A bibliometric review of advances in building information modeling (BIM) research, J. Eng. Des. Technol. 21 (2023), no. 3, 690–710.

[20] M. Baghalzadeh Shishehgarkhaneh, A. Keivani, R.C. Moehler, N. Jelodari, and S. Roshdi Laleh, Internet of things (IoT), building information modeling (BIM), and digital twin (DT) in construction industry: A review, bibliometric, and network analysis, Buildings 12 (2022), no. 10, 1503.

[21] P. Bhyan, B. Shrivastava, and N. Kumar, Systematic literature review of life cycle sustainability assessment system for residential buildings: Using bibliometric analysis 2000–2020, Environ. Dev. Sustain. 25 (2023), no. 12, 13637–13665.

[22] B. Blocken, J. Carmeliet, and T. Stathopoulos, CFD evaluation of wind speed conditions in passages between parallel buildings—effect of wall-function roughness modifications for the atmospheric boundary layer flow, J. Wind Eng. Ind. Aerodyn. 95 (2007), no. 9, 941–962.

[23] B. Blocken, T. Stathopoulos, J. Carmeliet, and J.L. M. Hensen, Application of computational fluid dynamics in building performance simulation for the outdoor environment: an overview, J. Build. Perform. Simul. 4 (2011), no. 2, 157–184.

[24] L. Bornmann and R. Mutz, Growth rates of modern science: A bibliometric analysis based on the number of publications and cited references, J. Assoc. Inf. Sci. Technol. 66 (2015), no. 11, 2215–2222.

[25] F. Bre, J.M. Gimenez, and V.D. Fachinotti, Prediction of wind pressure coefficients on building surfaces using artificial neural networks, Energy Build. 158 (2018), 1429–1441.

[26] A. Buonomano, F. Calise, A. Palombo, and M. Vicidomini, BIPVT systems for residential applications: An energy and economic analysis for European climates, Appl. Energy 184 (2016), 1411–1431.

[27] M. Campra, P. Esposito, and V. Brescia, State of the art of COVID-19 and business, management, and accounting sector. A bibliometrix analysis, Int. J. Bus. Manag. 16 (2020), no. 1, 35.

[28] A.A. Chadegani, H. Salehi, M.M. Yunus, H. Farhadi, M. Fooladi, M. Farhadi, and N.A. Ebrahim, A comparison between two main academic literature collections: Web of Science and Scopus databases, Asian Soc. Sci. 9 (2013), no. 5, 18.

[29] A.L.S. Chan, T.T. Chow, S.K.F. Fong, and J.Z. Lin, Generation of a typical meteorological year for Hong Kong, Energy Convers. Manag. 47 (2006), no. 1, 87–96.

[30] A. Chong, Y. Gu, and H. Jia, Calibrating building energy simulation models: A review of the basics to guide future work, Energy Build. 253 (2021), 111533.

[31] T.T. Chow, G. Pei, K. F. Fong, Z. Lin, A.L.S. Chan, and J. Ji, Energy and exergy analysis of photovoltaic–thermal collector with and without glass cover, Appl. Energy 86 (2009), no. 3, 310–316.

[32] D. Coakley, P. Raftery, and M. Keane, A review of methods to match building energy simulation models to measured data, Renew. Sustain. Energy Rev. 37 (2014), 12–141.

[33] J.W. Creswell, Research Design: Qualitative, Quantitative and Mixed Methods Approaches, 4th edition, Thousand Oaks: SAGE Publications, 2014.

[34] D.B. Crawley, J.W. Hand, M. Kummert, and B.T. Griffith, Contrasting the capabilities of building energy performance simulation programs, Build. Environ. 43 (2008), no. 4, 661–673.

[35] 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, and J. Glazer, Energy plus: Creating a new-generation building energy simulation

program, Energy Build. 33 (2001), no. 4, 319–331.

[36] D.B. Crawley, C.O. Pedersen, L.K. Lawrie, and F.C. Winkelmann, Energy plus: Energy simulation program, ASHRAE J. 42 (2000), no. 4, 49–56.

[37] M. A. Cusenza, F. Guarino, S. Longo, and M. Cellura, An integrated energy simulation and life cycle assessment to measure the operational and embodied energy of a Mediterranean net zero energy building, Energy Build. 254 (2022), 111558.

[38] S. Dabirian, K. Panchabikesan, and U. Eicker, Occupant-centric urban building energy modeling: Approaches, inputs, and data sources - A review, Energy Build. 257 (2022), 111809.

[39] E. Delzendeh, S. Wu, A. Lee, and Y. Zhou, The impact of occupants’ behaviors on building energy analysis: A research review, Renew. Sustain. Energy Rev. 80 (2017), 1061–1071.

[40] H. Dervis, Bibliometric Analysis using Bibliometrix an R Package, J. Scientometr. Res. 8 (2020), no. 3, 156–160.

[41] M.K. Dixit, C.H. Culp, and J.L. Fernandez-Solis, Embodied energy of construction materials: Integrating human and capital energy into an IO-based hybrid model, Environ. Sci. Technol. 49 (2015), no. 3, 1936–1945.

[42] M.K. Dixit, J.L. Fernandez-Solıs, S. Lavy, and C.H. Culp, Identification of parameters for embodied energy measurement: A literature review, Energy Build. 42 (2010), no. 8, 1238–1247.

[43] M.K. Dixit, J.L. Fernandez-Solıs, S. Lavy, and C.H. Culp, Need for an embodied energy measurement protocol for buildings: A review paper, Renew. Sustain. Energy Rev. 16 (2012), no. 6, 3730–3743.

[44] N. Donthu, S. Kumar, D. Mukherjee, N. Pandey, and W.M. Lim, How to conduct a bibliometric analysis: An overview and guidelines, J. Bus. Res. 133 (2021), 285–296.

[45] P. Dzikowski, A bibliometric analysis of born global firms, J. Bus. Res. 85 (2018), 281–294.

[46] N. Fumo, A review on the basics of building energy estimation, Renew. Sustain. Energy Rev. 31 (2014), 53–60.

[47] H. Ghaleb, H.H. Alhajlah, A.A. Bin Abdullah, M.A. Kassem, and M.A. Al-Sharafi, A scientometric analysis and systematic literature review for construction project complexity, Buildings 12 (2022), no. 4.

[48] Y. Ham and M. Golparvar-Fard, Mapping actual thermal properties to building elements in gbXML-based BIM for reliable building energy performance modeling, Autom. Constr. 49 (2015), 214–224.

[49] V.S.K.V. Harish and A. Kumar, A review on modeling and simulation of building energy systems, Renew. Sustain. Energy Rev. 56 (2016), 1272–1292.

[50] J.E. Hirsch, An index to quantify an individual’s scientific research output, Proc. Natl. Acad. Sci. 102 (2005), no. 46, 16569–16572.

[51] P. Hoes, J.L. M. Hensen, M.G.L.C. Loomans, B. de Vries, and D. Bourgeois, User behavior in whole building simulation, Energy Build. 41 (2009), no. 3, 295–302.

[52] T. Hong, S.C. Taylor-Lange, S. D’ Oca, D. Yan, and S.P. Corgnati, Advances in research and applications of energy-related occupant behavior in buildings, Energy Build. 116 (2016), 694–702.

[53] T. Hong, D. Yan, S. D’ Oca, and C. Chen, Ten questions concerning occupant behavior in buildings: The big picture, Build. Environ. 114 (2017), 518–530.

[54] C.J. Hopfe and J.L.M. Hensen, Uncertainty analysis in building performance simulation for design support, Energy Build. 43 (2011), no. 10, 2798–2805.

[55] M. Hosseini, A. Bigtashi, and B. Lee, Generating future weather files under climate change scenarios to support building energy simulation: A machine learning approach, Energy Build. 230 (2021), 110543.

[56] M. Hu and D. Milner, Visualizing the research of embodied energy and environmental impact research in the building and construction field: A bibliometric analysis, Dev. Built Environ. 3 (2020), 100010.

[57] M. Jia, R.S. Srinivasan, and A.A. Raheem, From occupancy to occupant behavior: An analytical survey of data acquisition technologies, modeling methodologies, and simulation coupling mechanisms for building energy efficiency, Renew. Sustain. Energy Rev. 68 (2017), 525–540.

[58] J. Kanters, M. Horvat, and M.C. Dubois, Tools and methods used by architects for solar design, Energy Build. 68 (2014), 721–731.

[59] E. Kamel and A.M. Memari, Review of BIM’s application in energy simulation: Tools, issues, and solutions, Autom. Constr. 97 (2019), 164–180.

[60] V. Koltun and D. Hafner, The h-index is no longer an effective correlate of scientific reputation, PLOS ONE 16 (2021), no. 6, 0253397.

[61] M. Krarti, P.M. Erickson, and T.C. Hillman, A simplified method to estimate energy savings of artificial lighting use from daylighting, Build. Environ. 40 (2005), no. 6, 747–754.

[62] S. Kumar, W.M. Lim, N. Pandey, and J. ChristopherWestland, 20 years of electronic commerce research, Electron. Commer. Res. 21 (2021), no. 1, 1–40.

[63] A. Laouadi, Development of a radiant heating and cooling model for building energy simulation software, Build. Environ. 39 (2004), no. 4, 421–431.

[64] R. A. Lara, E. Naboni, G. Pernigotto, F. Cappelletti, Y. Zhang, F. Barzon, A. Gasparella and P. Romagnoni, Optimization Tools for Building Energy Model Calibration, Energy Procedia 111 (2017), 1060–1069.

[65] Y. Li, Z. O’ Neill, L. Zhang, J. Chen, P. Im, and J. DeGraw, Grey-box modeling and application for building energy simulations: A critical review, Renew. Sustain. Energy Rev. 146 (2021), 111174.

[66] Y. Li, Y. Rong, U. M. Ahmad, X. Wang, J. Zuo, and G. Mao, A comprehensive review on green buildings research: bibliometric analysis during 1998–2018, Environ. Sci. Pollut. Res. 28 (2021), no. 34, 46196–46214.

[67] X. Li and J. Wen, Review of building energy modeling for control and operation, Renew. Sustain. Energy Rev. 37 (2014), 517–537.

[68] X. Li, P. Wu, G.Q. Shen, X. Wang, and Y. Teng, Mapping the knowledge domains of Building Information Modeling (BIM): A bibliometric approach, Autom. Constr. 84 (2017), 195–206.

[69] M.K. Linnenluecke, M. Marrone, and A.K. Singh, Conducting systematic literature reviews and bibliometric analyses, Aust. J. Manag. 45 (2020), no. 2, 175–194.

[70] T.J. Ma, G.G. Lee, J.S. Liu, R. Lan, and J.H. Weng, Bibliographic coupling: a main path analysis from 1963 to 2020, Inf. Res. Int. Electron. J. 27 (2022), no. 1.

[71] R. Mahmoud, J. M. Kamara and N. Burford, Opportunities and limitations of building energy performance simulation tools in the early stages of building design in the UK, Sustainability 12 (2020), no. 22, 9702.

[72] A. Malhotra, J. Bischof, A. Nichersu, K.H. Hafele, J. Exenberger, D. Sood, J. Allan, J. Frisch, C. van Treeck, J. O’Donnell, and G. Schweiger, Information modelling for urban building energy simulation-A taxonomic review, Build. Envir. 208 (2022), 108552.

[73] P. Marin, M. Saffari, A. Gracia, X. Zhu, M.M. Farid, L.F. Cabeza, and S. Ushak, Energy savings due to the use of PCM for relocatable lightweight buildings passive heating and cooling in different weather conditions, Energy Build. 129 (2016), 274–283.

[74] D. Mariano-Hernandez, L. Hernandez-Callejo, A. Zorita-Lamadrid, O. Duque-Perez, and F. Santos Garcia, A review of strategies for building energy management system: Model predictive control, demand side management, optimization, and fault detect & diagnosis, J. Build. Eng. 33 (2021), 101692.

[75] A. Maseda, T. Iturralde, S. Cooper and G. Aparicio, Mapping women’s involvement in family firms: A review based on bibliographic coupling analysis, Int. J. Manag. Rev. 24 (2022), no. 2, 279–305.

[76] M. Mirsadeghi, D. Costola, B. Blocken, and J.L. M. Hensen, Review of external convective heat transfer coefficient models in building energy simulation programs: Implementation and uncertainty, Appl. Therm. Eng. 56 (2013), no. 1-2, 134–151.

[77] D. Moher, L. Shamseer, M. Clarke, D. Ghersi, A. Liberati, M. Petticrew, P. Shekelle, L.A. Stewart, and PRISMA-P Group, Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement, Syst. Rev. 4 (2015), no. 1, 1.

[78] M. Moradi and M. Miralmasi, Pragmatic research method, F. Seydi (Ed.). School of Quantitative and Qualitative Research.MPT ACADEMY, 2020.

[79] J.A. Moral-Munoz, E. Herrera-Viedma, A. Santisteban-Espejo, and M.J. Cobo, Software tools for conducting bibliometric analysis in science: An up-to-date review, El Prof. Inf. 29 (2020), no. 1.

[80] A. Musbahi, C. B. Rao, and A. Immanuel, A Bibliometric Analysis of Robotic Surgery From 2001 to 2021, World J. Surg. 46 (2022), no. 6, 1314–1324.

[81] M.K. Najjar, K. Figueiredo, A.C.J. Evangelista, A.W.A. Hammad, V.W.Y. Tam, and A. Haddad, Life cycle assessment methodology integrated with BIM as a decision-making tool at early stages of building design, Int. J. Constr. Manag. 22 (2022), no. 4, 541–555.

[82] H. Omrany, R. Chang, V. Soebarto, Y. Zhang, A. Ghaffarianhoseini, and J. Zuo, A bibliometric review of net zero energy building research 1995–2022, Energy Build. 262 (2022), 111996.

[83] C. Oppenheim, The publish or perish book, Prometheus 29 (2011), no. 2, 181–183.

[84] A. Oraiopoulos and B. Howard, On the accuracy of urban building energy modelling, Renew. Sustain. Energy Rev. 158 (2022), 111976.

[85] M.J. Page, J.E. McKenzie, P.M. Bossuyt, I. Boutron, T.C. Hoffmann, C.D. Mulrow, L. Shamseer, J.M. Tetzlaff, E. A. Akl, S.E. Brennan, R. Chou, J. Glanville, J.M. Grimshaw, A. Hrobjartsson, M.M. Lalu, T. Li, E.W. Loder, E. Mayo-Wilson, S. McDonald, and D. Moher, The PRISMA 2020 statement: An updated guideline for reporting systematic reviews, BMJ 372 (2021), no. 71.

[86] J. Page, D. Robinson, N. Morel, and J.L. Scartezzini, A generalized stochastic model for the simulation of occupant presence, Energy Build. 40 (2008), no. 2, 83–98.

[87] Y. Pan, M. Zhu, Y. Lv, Y. Yang, Y. Liang, R. Yin, Y. Yang, X. Jia, X. Wang, F. Zeng, S. Huang, D. Hou, L. Xu, R. Yin, and X. Yuan, Building energy simulation and its application for building performance optimization: A review of methods, tools, and case studies, Adv. Appl. Energy 10 (2023), 100135.

[88] Z. Pang, Z. O’Neill, Y. Li, and F. Niu, The role of sensitivity analysis in the building performance analysis: A critical review, Energy Build. 209 (2020), 1096.

[89] B. Park, W.V. Srubar, and M. Krarti, Energy performance analysis of variable thermal resistance envelopes in residential buildings, Energy Build. 103 (2015), 317–325.

[90] L. Peeters, R. de Dear, J. Hensen, and W. D’haeseleer, Thermal comfort in residential buildings: Comfort values and scales for building energy simulation, Appl. Energy 86 (2009), no. 5, 772–780.

[91] V. Pereira, J. Santos, F. Leite, and P. Escorcio, Using BIM to improve building energy efficiency: A scientometric and systematic review, Energy Build. 250 (2021), 111292.

[92] L. Phan Tan, Bibliometrics of social entrepreneurship research: Cocitation and bibliographic coupling analyses, Cogent Bus. Manag. 9 (2022), no. 1, 2124 4, doi: 10.1080/23311975.2022.2124594.

[93] R. Pranckute, Web of Science (WoS) and Scopus: The titans of bibliographic information in today’s academic world, Publications 9 (2021), no. 1.

[94] A. Rasheed, W.H. Na, J.W. Lee, H.T. Kim, and H.W. Lee, Optimization of greenhouse thermal screens for maximized energy conservation, Energies 12 (2019), no. 19, 3592.

[95] E. Prataviera, J. Vivian, G. Lombardo, and A. Zarrella, Evaluation of the impact of input uncertainty on urban building energy simulations using uncertainty and sensitivity analysis, Appl. Energy 311 (2022), 118691.

[96] C. Ratti, N. Baker, and K. Steemers, Energy consumption and urban texture, Energy Build. 37 (2005), no. 7, 762–776.

[97] T.A. Reddy and K.K. Andersen, An evaluation of classical steady-state off-line linear parameter estimation methods applied to chiller performance data, HVACR Res. 8 (2002), no. 1, 101–124.

[98] K.N. Rhee and K.W. Kim, A 50-year review of basic and applied research in radiant heating and cooling systems for the built environment, Fifty Year Anniv. Build. Environ. 91 (2015), 166–190.

[99] N.D. Roman, F. Bre, V.D. Fachinotti, and R. Lamberts, Application and characterization of metamodels based on artificial neural networks for building performance simulation: A systematic review, Energy Build. 217 (2020), 109972.

[100] M. Saffari, A. de Gracia, S. Ushak, and L. F. Cabeza, Passive cooling of buildings with phase change materials using whole-building energy simulation tools: A review, Renew. Sustain. Energy Rev. 80 (2017), 1239–1255.

[101] D.J. Sailor, A green roof model for building energy simulation programs, Energy Build. 40 (2008), no. 8, 1466–1478.

[102] M. Santamouris, Cooling the cities: A review of reflective and green roof mitigation technologies to fight heat island and improve comfort in urban environments, Sol. Energy 103 (2014), 682–703.

[103] M. Santamouris, N. Papanikolaou, I. Livada, I. Koronakis, C. Georgakis, A. Argiriou, and D.N. Assimakopoulos, On the impact of urban climate on the energy consumption of buildings, Sol. Energy 70 (2001), no. 3, 201–216.

[104] M. Santamouris, A. Synnefa, and T. Karlessi, Using advanced cool materials in the urban built environment to mitigate heat islands and improve thermal comfort conditions, Sol. Energy 85 (2011), no. 12, 3085–3102.

[105] M. Schreiber, An empirical investigation of the g-index for 26 physicists in comparison with the h-index, the A-index, and the R-index, J. Am. Soc. Inf. Sci. Technol. 59 (2008), no. 9, 1513–1522.

[106] P.C. Tabares-Velasco, C. Christensen, and M. Bianchi, Verification and validation of EnergyPlus phase change material model for opaque wall assemblies, Build. Environ. 54 (2012), 186–196.

[107] A. Thomas, C.C. Menassa, and V.R. Kamat, A systems simulation framework to realize net-zero building energy retrofits, Sustain. Cities Soc. 41 (2018), 405–420.

[108] A. Thomas, C.C. Menassa, and V.R. Kamat, Lightweight and adaptive building simulation (LABS) framework for integrated building energy and thermal comfort analysis, Build. Simul. 10 (2017), no. 6, 1023–1044.

[109] W. Tian, A review of sensitivity analysis methods in building energy analysis, Renew. Sustain. Energy Rev. 20 (2013), 411–419.

[110] D. Tranfield, D. Denyer, and P. Smart, Towards a methodology for developing evidence-informed management knowledge using systematic review, Br. J. Manag. 14 (2003), no. 3, 207–222.

[111] D. Tuhus-Dubrow and M. Krarti, Genetic-algorithm based approach to optimize building envelope design for residential buildings, Build. Environ. 45 (2010), no. 7, 1574–1581.

[112] G. Verasoundarapandian, Z.S. Lim, S.B.M. Radziff, S.H. Taufik, N.A. Puasa, N.A. Shaharuddin, F. Merican, C.Y. Wong, J. Lalung, and S.A. Ahmad, Remediation of pesticides by microalgae as feasible approach in agriculture: Bibliometric strategies, Agronomy 12 (2022), no. 1, 117.

[113] O. Von Bohlen und Halbach, How to judge a book by its cover? How useful are bibliometric indices for the evaluation of ‘scientific quality’ or ‘scientific productivity’? Ann. Anat. Anat. Anz. 193 (2011), no. 3, 191–196.

[114] L. Wang, E.W.M. Lee, S.A. Hussian, A.C.Y. Yuen, and W. Feng, Quantitative impact analysis of driving factors on annual residential building energy end-use combining machine learning and stochastic methods, Appl. Energy 299 (2021), 117303.

[115] L. Wang and M.J. Witte, Integrating building energy simulation with a machine learning algorithm for evaluating indoor living walls’ impacts on cooling energy use in commercial buildings, Energy Build. 272 (2022), 112322.

[116] J.K.W. Wong and J. Zhou, Enhancing environmental sustainability over building life cycles through green BIM: A review, Autom. Constr. 57 (2015), 156–165.

[117] D. Yan, T. Hong, B. Dong, A. Mahdavi, S. D’Oca, I. Gaetani, and X. Feng, IEA EBC Annex 66: Definition and simulation of occupant behavior in buildings, Energy Build, 156 (2017), 258–270.

[118] C. Yu, J. Du and W. Pan, Improving accuracy in building energy simulation via evaluating occupant behaviors: A case study in Hong Kong, Energy Build. 202 (2019), 109373.

[119] R. Yu, Y. Li, Z. Zhang, Z. Gu, H. Zhong, Q. Zha, L. Yang, C. Zhu, and E. Chen, A bibliometric analysis using VOSviewer of publications on COVID-19, Ann. Transl. Med. 8 (2020), no. 13, 816.

[120] A. Zancanaro, J.L. Todesco, and F. Ramos, A bibliometric mapping of open educational resources, Int. Rev. Res. Open Distrib. Learn. 16 (2015), no. 1.

[121] Z. Zhang, A. Chong, Y. Pan, C. Zhang, and K. P. Lam, Whole building energy model for HVAC optimal control: A practical framework based on deep reinforcement learning, Energy Build. 199 (2019), 472–490.

[122] B. Zhong, H. Wu, H. Li, S. Sepasgozar, H. Luo, and L. He, A scientometric analysis and critical review of construction related ontology research, Autom. Constr. 101 (2019), 17–31.

[123] I. Zupic and T. Cater, Bibliometric Methods in Management and Organization, Organ. Res. Methods 18 (2015), no. 3, 429–472.