Journal Home > Volume 15 , Issue 7
Building Simulation 2022, 15(7): 1177-1192 https://doi.org/10.1007/s12273-021-0883-7
Cover Article | Issue | Published: 01 February 2022

BIM-based automated design for HVAC system of office buildings—An experimental study

Show Author's Information Hongxin Wang1,  Peng Xu1( ),  Huajing Sha1,  Jiefan Gu1,  Tong Xiao1,  Yikun Yang1,  Dingyi Zhang2
School of Mechanical Engineering, Tongji University, 1239 Siping Road, Shanghai, China
College of Food Science and Technology, Shanghai Ocean University, Shanghai 201306, China
Keywords:
HVAC system, BIM, BEM, automated design
Cite this article:
Wang H, Xu P, Sha H, et al. BIM-based automated design for HVAC system of office buildings—An experimental study. Building Simulation, 2022, 15(7): 1177-1192. https://doi.org/10.1007/s12273-021-0883-7
Download citation
EndNote(RIS)
BibTeX

1238

Views

128

Downloads

Citations

12

Crossref

9

WoS

11

Scopus

0

CSCD

Abstract Full text About this article

Although computer technologies have greatly advanced in recent years and help engineers improve work efficiency, the heating, ventilation, and air conditioning (HVAC) design process is still very time-consuming. In this paper, we propose a conceptual framework for automating the entire design process to replace current human-based HVAC design procedures. This framework includes the following automated processes: building information modeling (BIM) simplification, building energy modeling (BEM) generation & load calculation, HVAC system topology generation & equipment sizing, and system diagram generation. In this study, we analyze the importance of each process and possible ways to implement them using software. Then, we use a case study to test the automated design procedure and illustrate the feasibility of the new automated design approach. The purpose of this study is to simplify the steps in the traditional rule-based HVAC system design process by introducing artificial intelligence (AI) technology based on the traditional computer-aided design (CAD) process. Experimental results show that the automatic processes are feasible, compared with the traditional design process can effectively shorten the design time from 23.37 working hours to nearly 1 hour, and improve the efficiency.


menu
Abstract
Full text
Outline
About this article

BIM-based automated design for HVAC system of office buildings—An experimental study

Show Author's information Hongxin Wang1, Peng Xu1( ), Huajing Sha1, Jiefan Gu1, Tong Xiao1, Yikun Yang1, Dingyi Zhang2
School of Mechanical Engineering, Tongji University, 1239 Siping Road, Shanghai, China
College of Food Science and Technology, Shanghai Ocean University, Shanghai 201306, China

Abstract

Although computer technologies have greatly advanced in recent years and help engineers improve work efficiency, the heating, ventilation, and air conditioning (HVAC) design process is still very time-consuming. In this paper, we propose a conceptual framework for automating the entire design process to replace current human-based HVAC design procedures. This framework includes the following automated processes: building information modeling (BIM) simplification, building energy modeling (BEM) generation & load calculation, HVAC system topology generation & equipment sizing, and system diagram generation. In this study, we analyze the importance of each process and possible ways to implement them using software. Then, we use a case study to test the automated design procedure and illustrate the feasibility of the new automated design approach. The purpose of this study is to simplify the steps in the traditional rule-based HVAC system design process by introducing artificial intelligence (AI) technology based on the traditional computer-aided design (CAD) process. Experimental results show that the automatic processes are feasible, compared with the traditional design process can effectively shorten the design time from 23.37 working hours to nearly 1 hour, and improve the efficiency.

Keywords: HVAC system, BIM, BEM, automated design

References(38)

Ahmad MW, Mourshed M, Yuce B, et al. (2016). Computational intelligence techniques for HVAC systems: A review. Building Simulation, 9: 359–398.
DOI Google Scholar
Aichholzer O, Aurenhammer F, Alberts D, et al. (1996). A Novel Type of Skeleton for Polygons. New York: Springer.https://doi.org/10.1007/978-3-642-80350-5_65
DOI

Amasyali K, El-Gohary NM (2018). A review of data-driven building energy consumption prediction studies. Renewable and Sustainable Energy Reviews, 81: 1192–1205.
DOI Google Scholar
ASHRAE (2016). ANSI_ASHRAE_IES Standard 90.1-2016. Energy Standard for Buildings Except Low-Rise Residential Buildings. Atlanta, USA: American Society of Heating, Refrigerating and Air-Conditioning Engineer.

Asiedu Y, Besant RW, Gu P (2000). HVAC duct system design using genetic algorithms. HVAC & R Research, 6: 149–173.
DOI Google Scholar
AUTODESK (2020). RTS Calculation Method. Available at https://knowledge.autodesk.com/support/revit-products/learn-explore/caas/CloudHelp/cloudhelp/2021/ENU/Revit-Analyze/files/GUID-D88E8A06-6E08-4CD6-89A0-368A2AD6C03C-htm.html?us_oa=akn-us&us_si=971bce00-552f-40cf-9fdf-fbe21f9dc057&us_st=RTS%20Calculation%20Method.
Berquist J, Tessier A, O'Brien W, et al. (2017). An investigation of generative design for heating, ventilation, and air-conditioning. In: Proceedings of the Symposium on Simulation for Architecture and Urban Design, Toronto, Canada.
Brahme R, Mahdavi A, Lam K, et al. (2001). Complex building performance analysis in early stages of design. In: Proceedings of the 7th International IBPSA Conference, Brazil.
Brès A, Judex F, Suter G, et al. (2017). A method for automated generation of HVAC distribution subsystems for building performance simulation. In: Proceedings of the 15th IBPSA Conference, San Francisco, CA, USA.

Chen X, Yang H (2017). A multi-stage optimization of passively designed high-rise residential buildings in multiple building operation scenarios. Applied Energy, 206: 541–557.
DOI Google Scholar

Downing F, Flemming U (1981). The bungalows of Buffalo. Environment and Planning B: Planning and Design, 8: 269–293.
DOI Google Scholar
Fu Y (2016). Optimum design of HVAC water system configurations in high-rise buildings. Master Thesis, Tongji University, China.

Gao H, Koch C, Wu Y (2019). Building information modelling based building energy modelling: A review. Applied Energy, 238: 320–343.
DOI Google Scholar

Grubinger T, Chasparis GC, Natschläger T (2017). Generalized online transfer learning for climate control in residential buildings. Energy and Buildings, 139: 63–71.
DOI Google Scholar

Horowitz ESS, Anderson-Freed, S (2007). Fundamentals of Data Structures in C. NJ, USA: Silicon Press.

Jrade A, Jalaei F (2013). Integrating building information modelling with sustainability to design building projects at the conceptual stage. Building Simulation, 6: 429–444.
DOI Google Scholar
Kasmarik K (2010). A framework to integrate generative design techniques for enhancing design automation. In: Proceedings of the 15th International Conference on Computer Aided Architectural Design Research, Hong Kong, China.

Lin B, Chen H, Liu Y, et al. (2021). A preference-based multi- objective building performance optimization method for early design stage. Building Simulation, 14: 477–494.
DOI Google Scholar
Maile T, Fischer M, Bazjanac V (2007). Building energy performance simulation tools—A life-cycle and interoperable perspective. Center for Integrated Facility Engineering (CIFE) working papper, #WP107.
Merrell P, Schkufza E, Koltun V (2010). Computer-generated residential building layouts. In: Proceedings of the SA '10: SIGGRAPH ASIA 2010, Seoul, Korea.https://doi.org/10.1145/1882262.1866203
DOI

Miettinen R, Paavola S (2014). Beyond the BIM utopia: Approaches to the development and implementation of building information modeling. Automation in Construction, 43: 84–91.
DOI Google Scholar

Müller P, Wonka P, Haegler S, et al. (2006). Procedural modeling of buildings. ACM Transactions on Graphics, 25: 614–623.
DOI Google Scholar
Palonen M, Hasan A, Sirén K (2009). A genetic algorithm for optimization of building envelope andhvac system parameters. In: Proceedings of the 11th International IBPSA Conference— Building Simulation 2009, Glasgow, UK.

Petersen S, Svendsen S (2011). Method for simulating predictive control of building systems operation in the early stages of building design. Applied Energy, 88: 4597–4606.
DOI Google Scholar

Schlueter A, Thesseling F (2009). Building information model based energy/exergy performance assessment in early design stages. Automation in Construction, 18: 153–163.
DOI Google Scholar

Sha H, Xu P, Yang Z, et al. (2019). Overview of computational intelligence for building energy system design. Renewable and Sustainable Energy Reviews, 108: 76–90.
DOI Google Scholar
Shalizi C (2002). A New Kind of Science by Stephen Wolfram.

Song F, Zhao B, Yang X, et al. (2008). A new approach on zonal modeling of indoor environment with mechanical ventilation. Building and Environment, 43: 278–286.
DOI Google Scholar

Sönmez NO (2018). A review of the use of examples for automating architectural design tasks. Computer-Aided Design, 96: 13–30.
DOI Google Scholar

Stiny G, Mitchell WJ (1978). The Palladian grammar. Environment and Planning B: Planning and Design, 5: 5–18.
DOI Google Scholar

Tuhus-Dubrow D, Krarti M (2010). Genetic-algorithm based approach to optimize building envelope design for residential buildings. Building and Environment, 45: 1574–1581.
DOI Google Scholar

Widodo A, Yang BS (2007). Support vector machine in machine condition monitoring and fault diagnosis. Mechanical Systems and Signal Processing, 21: 2560–2574.
DOI Google Scholar
Wight J, Zhang Y (2005). An "ageing" operator and its use in the highly constrained topological optimization of HVAC system design. In: Proceedings of the 7th Annual Conference on Genetic and Evolutionary Computation, Washington DC, USA.https://doi.org/10.1145/1068009.1068353
DOI

Wong JKW, Zhou J (2015). Enhancing environmental sustainability over building life cycles through green BIM: A review. Automation in Construction, 57: 156–165.
DOI Google Scholar

Wong KD, Fan Q (2013). Building information modelling (BIM) for sustainable building design. Facilities, 31: 138–157.
DOI Google Scholar
Wright J, Farmani R (2001). The simultaneous optimization of building fabric construction, HVAC system size, and the plant control strategy. In: Proceedings of the 7th International IBPSA Conference, Brazil.

Wright J, Zhang Y, Angelov P, et al. (2008). Evolutionary synthesis of HVAC system configurations: algorithm development (RP-1049). HVAC & R Research, 14: 33–55.
DOI Google Scholar

Yeo TTE, Jin XC, Ong SH, et al. (1994). Clump splitting through concavity analysis. Pattern Recognition Letters, 15: 1013–1018.
DOI Google Scholar
Publication history
Copyright
Acknowledgements

Publication history

Received: 26 June 2021
Revised: 14 December 2021
Accepted: 23 December 2021
Published: 01 February 2022
Issue date: July 2022

Copyright

© Tsinghua University Press 2022

Acknowledgements

Acknowledgements

This research is supported by China Southern Power Grid Co. LTD for the Science and Technology Project (Grant No. GDKJXM20212099).

Return