Abstract
Building Information Modelling (BIM) is a technique that facilitates the Architect, Engineering, Construction, and Operation (AECO) practitioners to model the building with detailed information in the early design phase. BIM is increasingly in demand as it provides a reliable platform for deciding the life cycle of a building at any time. We can say that BIM is the process of building a building twice. In the planning phase, the building is created in the virtual environment. All the procurement management phases, i.e., planning, building, scheduling, costing, operating and managing are simulated. The operation phase of any building has different aspects such as locating building components, facilitating real-time data access, space management, energy management, demolition and renovation. This paper focuses on the energy management segment. With ongoing increases in energy costs, rising climatic consequences and updating building guidelines worldwide, AECO practitioners progressively have to consider the building’s energy performance while planning. The buildings’ energy utilization pattern depends upon various factors like daylight, HVAC system, location, orientation, resident’s behaviour, building material and weather. Current energy analysis systems are satisfactory up to limited levels; hence, there is a need for more efficient energy simulation systems. BIM-integrated energy simulation techniques can play a crucial role in efficiently analysing the building’s performance and enhancing it. These methods are termed as Building Energy Modelling (BEM). Different BEM tools work based on input data and considerations, significantly affecting a particular tool's capacity. In this paper, different techniques are discussed accordingly. The energy modelling process is carried out by transferring the building data to a BEM tool in an acceptable file format. This research provides deep insights into the advantages and disadvantages of various methods that contribute to future research and development of advanced methods and software programs.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abanda FH, Byers L (2016) An investigation of the impact of building orientation on energy consumption in a domestic building using emerging BIM (building information modelling). Energy 97:517–527. https://doi.org/10.1016/j.energy.2015.12.135
Asl MR, Bergin M, Menter A, Yan W (2014) BIM-Based parametric building energy performance multiobjective optimization. In: 32nd ECAADe conference, vol 224, p 10. http://autodeskresearch.com/pdf/bimparametric.pdf
Bazjanac V (2009) Implementation of semi-automated energy performance simulation: building geometry. In: Managing IT in construction/managing construction for tomorrow, pp 613–20. https://doi.org/10.1201/9781482266665-84
Borrmann A, König M, Koch C, Beetz J (2018) Building information modeling: Why? What? How? Build Inf Model Technol Found Ind Pract 1–24. https://doi.org/10.1007/978-3-319-92862-3_1
Bryde D, Broquetas M, Volm JM (2013) The project benefits of building information modelling (BIM). Int J Project Manage 31(7):971–980. https://doi.org/10.1016/j.ijproman.2012.12.001
Crawley DB, Lawrie LK, Winkelmann FC, Buhl WF, Joe Huang Y, Pedersen CO, Strand RK et al (2001) EnergyPlus: creating a new-generation building energy simulation program. Energy Build 33(4):319–331. https://doi.org/10.1016/S0378-7788(00)00114-6
Dong B, Lam KP, Huang YC, Dobbs GM (2007) A comparative study of the IFC and GbXML informational infrastructures for data exchange in computational design support environments. In: IBPSA 2007—international building performance simulation association 2007, pp 1530–37. https://www.researchgate.net/publication/285494452_A_comparative_study_of_the_IFC_and_gbXML_informational_infrastructures_for_data_exchange_in_computational_design_support_environments
Enshassi A, Ayash A, Mohamed S (2018) Factors driving contractors to implement energy management strategies in construction projects. J Financ Manag Prop Constr 23(3):295–311. https://doi.org/10.1108/JFMPC-09-2017-0035
Giannakis GI, Lilis GN, Garcia MA, Kontes GD, Valmaseda C, Rovas DV (2015) A methodology to automatically generate geometry inputs for energy performance simulation from IFC BIM models. In: 14th International conference of IBPSA—building simulation 2015, BS 2015, conference proceedings, pp 504–11
Ham Y, Golparvar-Fard M (2015) Mapping actual thermal properties to building elements in GbXML-Based BIM for reliable building energy performance modeling. Autom Constr 49:214–224. https://doi.org/10.1016/j.autcon.2014.07.009
Hviid CA, Nielsen TR, Svendsen S (2008) Simple tool to evaluate the impact of daylight on building energy consumption. Sol Energy 82(9):787–798. https://doi.org/10.1016/j.solener.2008.03.001
Jaradat S (2014) Educating the next generation of architects for interdisciplinary BIM environments. Charrette 1(1):127–136
Kim H, Shen Z, Kim I, Kim K, Stumpf A, Jungho Y (2016) BIM IFC information mapping to building energy analysis (BEA) model with manually extended material information. Autom Constr 68:183–193. https://doi.org/10.1016/j.autcon.2016.04.002
Kim I, Kim J, Seo J (2012) Development of an IFC-Based IDF converter for supporting energy performance assessment in the early design phase. J Asian Archit Build Eng 11(2):313–320. https://doi.org/10.3130/jaabe.11.313
Kim S, Zadeh PA, Staub-French S, Froese T, Cavka BT (2016) Assessment of the impact of window size, position and orientation on building energy load using BIM. Procedia Eng 145:1424–1431. https://doi.org/10.1016/j.proeng.2016.04.179
Laine T, Karola A (2007) Benefits of building information models in energy analysis. Energy 8. http://www.irbnet.de/daten/iconda/CIB8170.pdf
Lilis GN, Giannakis GI, Rovas DV (2017) Automatic generation of second-level space boundary topology from IFC geometry inputs. Autom Constr 76:108–124. https://doi.org/10.1016/j.autcon.2016.08.044
Liu R, Issa RRA (2016) Survey: common knowledge in BIM for facility maintenance. J Perform Constructed Facil 30(3). https://doi.org/10.1061/(ASCE)CF.1943-5509.0000778
Røpke I, Christensen TH (2012) Energy impacts of ICT—insights from an everyday life perspective. Telematics Inform 29(4):348–361. https://doi.org/10.1016/j.tele.2012.02.001
Wong AKD, Wong FKW, Nadeem A (2013) Comparative roles of major stakeholders for the implementation of BIM in various countries. J Chem Inf Model 53(9):1689–99
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Shah, D., Kathiriya, H., Suthar, H., Pandya, P., Soni, J. (2023). Enhancing the Building's Energy Performance through Building Information Modelling—A Review. In: Ranadive, M.S., Das, B.B., Mehta, Y.A., Gupta, R. (eds) Recent Trends in Construction Technology and Management. Lecture Notes in Civil Engineering, vol 260. Springer, Singapore. https://doi.org/10.1007/978-981-19-2145-2_20
Download citation
DOI: https://doi.org/10.1007/978-981-19-2145-2_20
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-2144-5
Online ISBN: 978-981-19-2145-2
eBook Packages: EngineeringEngineering (R0)