A BIBLIOMETRIC ANALYSIS OF BUILDING ENERGY SIMULATION AND OCCUPANT BEHAVIOR CULTURE ON SCOPUS DATABASE
Subject Areas : Development
Somayeh Dowlat
1
,
Ghazal Safdarian
2
,
Heidar Jahanbakhsh
3
,
Fahime Motazedian
4
1 - Department of Architecture, Pardis Branch, Islamic Azad University, Pardis, Iran
2 - Assistant Professor Department of Architecture, Pardis Branch, Islamic Azad University, Pardis, Iran
3 - Associate Professor, Department Of Architecture, Tehran Branch, Payame Noor University, Tehran, Iran
4 - Assistant Professor Department Of Architecture, Pardis Branch, Islamic Azad University, Pardis, Iran
Keywords: Energy Simulation, Buildings, Occupant Behavior Culture, Bibliometric Approach, Citation Analysis, Scopus.,
Abstract :
The use of fossil fuels and resulting greenhouse gas emissions are the primary causes of climate change, which are among the major challenges facing humanity currently and in the future. Buildings, accounting for half of the world's energy consumption during construction and operation, have a significant potential for energy reduction. One of the fundamental solutions for estimating and reducing energy consumption in buildings is the use of energy simulation. The aim of this study is to comprehensively review the existing literature in the field of energy simulation in buildings based on previous studies to provide a complete description of the research conducted in this area. Using a systematic research method, information extracted from the Scopus database between 1982 and 2022 was preprocessed and classified. By examining 2929 scientific documents and considering the functional objectives of bibliometric studies, trends and declines, the most important articles, authors, and countries in the field were identified. Additionally, with regard to network objectives, influential co-citation patterns were identified, and then hotspots in the field were identified through explicit content analysis of keywords. Finally, gaps and future research trends in the field of energy simulation in buildings were identified and introduced.
Abdullah, K. H. (2021). Mapping of marine safety publications using VOSviewer. ASM Science Journal, 16, 1–9.
Abolhassani, S. S., Amayri, M., Bouguila, N., & Eicker, U. (2022). A new workflow for detailed urban scale building energy modeling using spatial joining of attributes for archetype selection. Journal of Building Engineering, 46, 103661.
Alnajem, M., Mostafa, M. M., & ElMelegy, A. R. (2021). Mapping the first decade of circular economy research: A bibliometric network analysis. Journal of Industrial and Production Engineering, 38(1), 29–50.
Alwetaishi, M. (2022). Can we learn from heritage buildings to achieve nearly zero energy building and thermal comfort? A case study in a hot climate. Advances in Building Energy Research, 16(2), 214–230.
Andersen, R., Fabi, V., Toftum, J., Corgnati, S. P., & Olesen, B. W. (2013). Window opening behaviour modelled from measurements in Danish dwellings. Building and Environment, 69, 101–113.
Ascione, F., Bianco, N., De Masi, R. F., de’Rossi, F., & Vanoli, G. P. (2014). Energy refurbishment of existing buildings through the use of phase change materials: Energy savings and indoor comfort in the cooling season. Applied Energy, 113, 990–1007.
Attia, S. (2010). Building performance simulation tools: Selection criteria and user survey.
Azar, E., & Menassa, C. C. (2012). A comprehensive analysis of the impact of occupancy parameters in energy simulation of office buildings. Energy and Buildings, 55, 841–853.
Babalola, A., Musa, S., Akinlolu, M. T., & Haupt, T. C. (2021). A bibliometric review of advances in building information modeling (BIM) research. Journal of Engineering, Design and Technology, ahead-of-print.
Baghalzadeh Shishehgarkhaneh, M., Keivani, A., Moehler, R. C., Jelodari, N., & Roshdi Laleh, S. (2022). Internet of Things (IoT), Building Information Modeling (BIM), and Digital Twin (DT) in Construction Industry: A Review, Bibliometric, and Network Analysis. Buildings, 12(10), 1503.
Bhyan, P., Shrivastava, B., & Kumar, N. (2022). Systematic literature review of life cycle sustainability assessment system for residential buildings: Using bibliometric analysis 2000–2020. Environment, Development and Sustainability, 1–29.
Bornmann, L., & Mutz, R. (2014). Growth rates of modern science: A bibliometric analysis. Journal of the Association for Information Science and Technology.
Cabeza, L. F., Castell, A., Barreneche, C. de, De Gracia, A., & Fernández, A. I. (2011). Materials used as PCM in thermal energy storage in buildings: A review. Renewable and Sustainable Energy Reviews, 15(3), 1675–1695.
Calì, D., Andersen, R. K., Müller, D., & Olesen, B. W. (2016). Analysis of occupants’ behavior related to the use of windows in German households. Building and Environment, 103, 54–69.
Campra, M., Paolo, E., & Brescia, V. (2021). State of the art of COVID-19 and business, management, and accounting sector. A bibliometrix analysis. International Journal of Business and Management, 16(1), 1–35.
Chan, A. L., Chow, T.-T., Fong, S. K., & Lin, J. Z. (2006). Generation of a typical meteorological year for Hong Kong. Energy Conversion and Management, 47(1), 87–96.
Chong, A., Gu, Y., & Jia, H. (2021). Calibrating building energy simulation models: A review of the basics to guide future work. Energy and Buildings, 253, 111533. https://doi.org/10.1016/j.enbuild.2021.111533.
Coakley, D., Raftery, P., & Keane, M. (2014). A review of methods to match building energy simulation models to measured data. Renewable and Sustainable Energy Reviews, 37, 123–141.
Crawley, D. B., Hand, J. W., Kummert, M., & Griffith, B. T. (2008). Contrasting the capabilities of building energy performance simulation programs. Building and Environment, 43(4), 661–673. https://doi.org/10.1016/j.buildenv.2006.10.027.
Crawley, D. B., Lawrie, L. K., Winkelmann, F. C., Buhl, W. F., Huang, Y. J., Pedersen, C. O., Strand, R. K., Liesen, R. J., Fisher, D. E., & Witte, M. J. (2001). EnergyPlus: Creating a new-generation building energy simulation program. Energy and Buildings, 33(4), 319–331.
Crawley, D., Pedersen, C., Lawrie, L., & Winkelmann, F. (2000). EnergyPlus: Energy Simulation Program. Ashrae Journal, 42, 49–56.
Creswell, J. W., & Creswell, J. D. (2017). Research design: Qualitative, quantitative, and mixed methods approaches. Sage publications.
Cusenza, M. A., Guarino, F., Longo, S., & Cellura, M. (2022). An integrated energy simulation and life cycle assessment to measure the operational and embodied energy of a Mediterranean net zero energy building. Energy and Buildings, 254, 111558.
Dalla Rosa, A., & Christensen, J. E. (2011). Low-energy district heating in energy-efficient building areas. Energy, 36(12), 6890–6899.
Delzendeh, E., Wu, S., Lee, A., & Zhou, Y. (2017). The impact of occupants’ behaviours on building energy analysis: A research review. Renewable and Sustainable Energy Reviews, 80, 1061–1071.
Diao, L., Sun, Y., Chen, Z., & Chen, J. (2017). Modeling energy consumption in residential buildings: A bottom-up analysis based on occupant behavior pattern clustering and stochastic simulation. Energy and Buildings, 147, 47–66.
Dixit, M. K., Fernández-Solís, J. L., Lavy, S., & Culp, C. H. (2010). Identification of parameters for embodied energy measurement: A literature review. Energy and Buildings, 42(8), 1238–1247.
Dixit, M. K., Fernández-Solís, J. L., Lavy, S., & Culp, C. H. (2012). Need for an embodied energy measurement protocol for buildings: A review paper. Renewable and Sustainable Energy Reviews, 16(6), 3730–3743. https://doi.org/10.1016/j.rser.2012.03.021.
D’Oca, S., & Hong, T. (2015). Occupancy schedules learning process through a data mining framework. Energy and Buildings, 88, 395–408.
Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070.
Duarte, C., Van Den Wymelenberg, K., & Rieger, C. (2013). Revealing occupancy patterns in an office building through the use of occupancy sensor data. Energy and Buildings, 67, 587–595.
Gaetani, I., Hoes, P.-J., & Hensen, J. L. (2016). Occupant behavior in building energy simulation: Towards a fit-for-purpose modeling strategy. Energy and Buildings, 121, 188–204.
Geng, S., Wang, Y., Zuo, J., Zhou, Z., Du, H., & Mao, G. (2017). Building life cycle assessment research: A review by bibliometric analysis. Renewable and Sustainable Energy Reviews, 76, 176–184.
Ghaleb, H., Alhajlah, H. H., Bin Abdullah, A. A., Kassem, M. A., & Al-Sharafi, M. A. (2022). A scientometric analysis and systematic literature review for construction project complexity. Buildings, 12(4), 482.
Gunay, H. B., O’Brien, W., & Beausoleil-Morrison, I. (2013). A critical review of observation studies, modeling, and simulation of adaptive occupant behaviors in offices. Building and Environment, 70, 31–47.
Gutiérrez-Salcedo, M., Martínez, M. Á., Moral-Munoz, J. A., Herrera-Viedma, E., & Cobo, M. J. (2018). Some bibliometric procedures for analyzing and evaluating research fields. Applied Intelligence, 48, 1275–1287.
Harish, V. S. K. V., & Kumar, A. (2016). A review on modeling and simulation of building energy systems. Renewable and Sustainable Energy Reviews, 56, 1272–1292. https://doi.org/10.1016/j.rser.2015.12.040.
Harzing -, A.-W. (2016, February 6). Publish or Perish. Harzing.Com. https://harzing.com/resources/publish-or-perish.
Hong, T., D’Oca, S., Turner, W. J., & Taylor-Lange, S. C. (2015). An ontology to represent energy-related occupant behavior in buildings. Part I: Introduction to the DNAs framework. Building and Environment, 92, 764–777.
Hong, T., & Lin, H.-W. (2013). Occupant behavior: Impact on energy use of private offices. Lawrence Berkeley National Lab.(LBNL), Berkeley, CA (United States).
Hong, T., Sun, H., Chen, Y., Taylor-Lange, S. C., & Yan, D. (2016). An occupant behavior modeling tool for co-simulation. Energy and Buildings, 117, 272–281.
Hong, T., Taylor-Lange, S. C., D’Oca, S., Yan, D., & Corgnati, S. P. (2016). Advances in research and applications of energy-related occupant behavior in buildings. Energy and Buildings, 116, 694–702.
Hong, T., Yan, D., D’Oca, S., & Chen, C. (2017). Ten questions concerning occupant behavior in buildings: The big picture. Building and Environment, 114, 518–530.
Hosseini, M., Bigtashi, A., & Lee, B. (2021). Generating future weather files under climate change scenarios to support building energy simulation–A machine learning approach. Energy and Buildings, 230, 110543.
Im, P., Joe, J., Bae, Y., & New, J. R. (2020). Empirical validation of building energy modeling for multi-zones commercial buildings in cooling season. Applied Energy, 261, 114374.
Jia, M., Srinivasan, R. S., & Raheem, A. A. (2017). From occupancy to occupant behavior: An analytical survey of data acquisition technologies, modeling methodologies and simulation coupling mechanisms for building energy efficiency. Renewable and Sustainable Energy Reviews, 68, 525–540.
Jin, Y., Yan, D., Kang, X., Chong, A., & Zhan, S. (2021). Forecasting building occupancy: A temporal-sequential analysis and machine learning integrated approach. Energy and Buildings, 252, 111362.
Li, X., Wu, P., Shen, G. Q., Wang, X., & Teng, Y. (2017). Mapping the knowledge domains of Building Information Modeling (BIM): A bibliometric approach. Automation in Construction, 84, 195–206.
Li, Y., Leung, G. M., Tang, J. W., Yang, X., Chao, C. Y., Lin, J. Z., Lu, J. W., Nielsen, P. V., Niu, J., & Qian, H. (2007). Role of ventilation in airborne transmission of infectious agents in the built environment-a multidisciplinary systematic review. Indoor Air, 17(1), 2–18.
Li, Y., Rong, Y., Ahmad, U. M., Wang, X., Zuo, J., & Mao, G. (2021). A comprehensive review on green buildings research: Bibliometric analysis during 1998–2018. Environmental Science and Pollution Research, 28, 46196–46214.
Linnenluecke, M. K., Marrone, M., & Singh, A. K. (2020). Conducting systematic literature reviews and bibliometric analyses. Australian Journal of Management, 45(2), 175–194. https://doi.org/10.1177/0312896219877678.
Mo, J., Zhang, Y., Xu, Q., Lamson, J. J., & Zhao, R. (2009). Photocatalytic purification of volatile organic compounds in indoor air: A literature review. Atmospheric Environment, 43(14), 2229–2246.
Moher, D., Shamseer, L., Clarke, M., Ghersi, D., Liberati, A., Petticrew, M., Shekelle, P., Stewart, L. A., & PRISMA-P Group. (2015). Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Systematic Reviews, 4(1), 1. https://doi.org/10.1186/2046-4053-4-1.
Moradi.M, & Miralmasi.M. (2020). Pragmatic research method ((F. Seydi, Ed.) (1st ed.)). School of quantitative and qualitative research. https://analysisacademy.com.
Moral-Munoz, J., Herrera-Viedma, E., Espejo, A., & Cobo, M. (2020). Software tools for conducting bibliometric analysis in science: An up-to-date review. El Profesional de La Información, 29. https://doi.org/10.3145/epi.2020.ene.03.
Omrany, H., Chang, R., Soebarto, V., Zhang, Y., Ghaffarianhoseini, A., & Zuo, J. (2022). A bibliometric review of net zero energy building research 1995–2022. Energy and Buildings, 111996.
Østergård, T., Jensen, R. L., & Maagaard, S. E. (2016). Building simulations supporting decision making in early design–A review. Renewable and Sustainable Energy Reviews, 61, 187–201.
Page, J., Robinson, D., Morel, N., & Scartezzini, J.-L. (2008). A generalised stochastic model for the simulation of occupant presence. Energy and Buildings, 40(2), 83–98.
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, n71. https://doi.org/10.1136/bmj.n71.
Pan, Y., Huang, Z., & Wu, G. (2007). Calibrated building energy simulation and its application in a high-rise commercial building in Shanghai. Energy and Buildings, 39(6), 651–657.
Pan, Y., Zhu, M., Lv, Y., Yang, Y., Liang, Y., Yin, R., Yang, Y., Jia, X., Wang, X., & Zeng, F. (2023). Building energy simulation and its application for building performance optimization: A review of methods, tools, and case studies. Advances in Applied Energy, 100135.
Panchabikesan, K., Haghighat, F., & El Mankibi, M. (2021). Data driven occupancy information for energy simulation and energy use assessment in residential buildings. Energy, 218, 119539.
Pereira, V., Santos, J., Leite, F., & Escórcio, P. (2021). Using BIM to improve building energy efficiency–A scientometric and systematic review. Energy and Buildings, 250, 111292.
Pranckutė, R. (2021). Web of Science (WoS) and Scopus: The Titans of Bibliographic Information in Today’s Academic World. Publications, 9(1), Article 1. https://doi.org/10.3390/publications9010012.
Prataviera, E., Vivian, J., Lombardo, G., & Zarrella, A. (2022). Evaluation of the impact of input uncertainty on urban building energy simulations using uncertainty and sensitivity analysis. Applied Energy, 311, 118691.
Santamouris, M., Papanikolaou, N., Livada, I., Koronakis, I., Georgakis, C., Argiriou, A., & Assimakopoulos, D. N. (2001). On the impact of urban climate on the energy consumption of buildings. Solar Energy, 70(3), 201–216.
Santos, R., Costa, A. A., & Grilo, A. (2017). Bibliometric analysis and review of Building Information Modelling literature published between 2005 and 2015. Automation in Construction, 80, 118–136.
Tian, W. (2013). A review of sensitivity analysis methods in building energy analysis. Renewable and Sustainable Energy Reviews, 20, 411–419. https://doi.org/10.1016/j.rser.2012.12.014.
Toparlar, Y., Blocken, B., Maiheu, B., & Van Heijst, G. J. F. (2017). A review on the CFD analysis of urban microclimate. Renewable and Sustainable Energy Reviews, 80, 1613–1640.
Van Nunen, K., Li, J., Reniers, G., & Ponnet, K. (2018). Bibliometric analysis of safety culture research. Safety Science, 108, 248–258.
Wallin, J. A. (2005). Bibliometric methods: Pitfalls and possibilities. Basic & Clinical Pharmacology & Toxicology, 97(5), 261–275.
Wang, C., Yan, D., & Jiang, Y. (2011). A novel approach for building occupancy simulation. Building Simulation, 4, 149–167.
Wang, L., Lee, E. W., Hussian, S. A., Yuen, A. C. Y., & Feng, W. (2021). Quantitative impact analysis of driving factors on annual residential building energy end-use combining machine learning and stochastic methods. Applied Energy, 299, 117303.
Wang, L., & Witte, M. J. (2022). Integrating building energy simulation with a machine learning algorithm for evaluating indoor living walls’ impacts on cooling energy use in commercial buildings. Energy and Buildings, 272, 112322.
Wong, J. K. W., & Zhou, J. (2015). Enhancing environmental sustainability over building life cycles through green BIM: A review. Automation in Construction, 57, 156–165.
Yan, D., Hong, T., Dong, B., Mahdavi, A., D’Oca, S., Gaetani, I., & Feng, X. (2017). IEA EBC Annex 66: Definition and simulation of occupant behavior in buildings. Energy and Buildings, 156, 258–270.
Yan, D., O’Brien, W., Hong, T., Feng, X., Gunay, H. B., Tahmasebi, F., & Mahdavi, A. (2015). Occupant behavior modeling for building performance simulation: Current state and future challenges. Energy and Buildings, 107, 264–278.
Yu, C., Du, J., & Pan, W. (2019). Improving accuracy in building energy simulation via evaluating occupant behaviors: A case study in Hong Kong. Energy and Buildings, 202, 109373.
Yu, Y., Li, Y., Zhang, Z., Gu, Z., Zhong, H., Zha, Q., Yang, L., Zhu, C., & Chen, E. (2020). A bibliometric analysis using VOSviewer of publications on COVID-19. Annals of Translational Medicine, 8(13).
Zalba, B., Marın, J. M., Cabeza, L. F., & Mehling, H. (2003). Review on thermal energy storage with phase change: Materials, heat transfer analysis and applications. Applied Thermal Engineering, 23(3), 251–283.
Zhang, Z., Chong, A., Pan, Y., Zhang, C., & Lam, K. P. (2019). Whole building energy model for HVAC optimal control: A practical framework based on deep reinforcement learning. Energy and Buildings, 199, 472–490.
Zhong, B., Wu, H., Li, H., Sepasgozar, S., Luo, H., & He, L. (2019). A scientometric analysis and critical review of construction related ontology research. Automation in Construction, 101, 17–31.