Abstract
This study developed a rapid building modeling tool, AutoBPS-BIM, to transfer the building information model (BIM) to the building energy model (BEM) for load calculation and chiller design optimization. An eight-storey office building in Beijing, 33.2 m high, 67.2 m long and 50.4 m wide, was selected as a case study building. First, a module was developed to transfer BIM in IFC format into BEM in EnergyPlus. Variable air volume systems were selected for the air system, while water-cooled chillers and boilers were used for the central plant. The EnergyPlus model calculated the heating and cooling loads for each space as well as the energy consumption of the central plant. Moreover, a chiller optimization module was developed to select the optimal chiller design for minimizing energy consumption while maintaining thermal comfort. Fifteen available chillers were included, with capacities ranging from 471 kW to 1329 kW. The results showed that the cooling loads of the spaces ranged from 33 to 100 W/m2 with a median of 45 W/m2, and the heating load ranged from 37 to 70 W/m2 with a median of 52 W/m2. The central plant’s total cooling load under variable air volume systems was 1400 kW. Compared with the static load calculation method, the dynamic method reduced 33% of the chiller design capacity. When two chillers were used, different chiller combinations’ annual cooling energy consumption ranged from 10.41 to 11.88, averaging 11.12 kWh/m2. The lowest energy consumption was 10.41 kWh/m2 when two chillers with 538 kW and 1076 kW each were selected. Selecting the proper chiller number with different capacities was critical to achieving lower energy consumption, which achieved 12.6% cooling system energy consumption reduction for the case study building. This study demonstrated that AutoBPS-BIM has a large potential in modeling BEM and optimizing chiller design.
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References
Building Energy Research Center of Tsinghua University (2022). 2022 Annual Report on China Building Efficiency. Beijing: China Architecture & Building Press. (in Chinese)
Chen Y, Yang C, Pan X, et al. (2020). Design and operation optimization of multi-chiller plants based on energy performance simulation. Energy and Buildings, 222: 110100.
Chen Z, Chen Y, Yang C (2022). Impacts of large chilled water temperature difference on thermal comfort, equipment sizes, and energy saving potential. Journal of Building Engineering, 49: 104069.
Cheng Q, Wang S, Yan C, et al. (2017). Probabilistic approach for uncertainty-based optimal design of chiller plants in buildings. Applied Energy, 185: 1613–1624.
Dai M, Lu X, Xu P (2021). Causes of low delta-T syndrome for chilled water systems in buildings. Journal of Building Engineering, 33: 101499.
de Lima Montenegro Duarte JGC, Ramos Zemero B, de Souza ACDB, et al. (2021). Building Information Modeling approach to optimize energy efficiency in educational buildings. Journal of Building Engineering, 43: 102587.
Deng Z, Chen Y, Yang J, et al. (2022). Archetype identification and urban building energy modeling for city-scale buildings based on GIS datasets. Building Simulation, 15: 1547–1559.
Deng Z, Chen Y, Yang J, et al. (2023). AutoBPS: A tool for urban building energy modeling to support energy efficiency improvement at city-scale. Energy and Buildings, 282: 112794.
DOE (2021). EnergyPlus Version 9.6.0 Engineering Reference. U.S. Department of Energy.
Farzaneh A, Monfet D, Forgues D (2019). Review of using Building Information Modeling for building energy modeling during the design process. Journal of Building Engineering, 23: 127–135.
Gang W, Wang S, Shan K, Gao D (2015). Impacts of cooling load calculation uncertainties on the design optimization of building cooling systems. Energy and Buildings, 94: 1–9.
Gao H, Koch C, Wu Y (2019). Building information modelling based building energy modelling: A review. Applied Energy, 238: 320–343.
He Y, Chen Y, Chen Z, et al. (2022). Impacts of occupant behavior on building energy consumption and energy savings analysis of upgrading ASHRAE 90.1 energy efficiency standards. Buildings, 12: 1108.
Huang P, Huang G, Augenbroe G, et al. (2018). Optimal configuration of multiple-chiller plants under cooling load uncertainty for different climate effects and building types. Energy and Buildings, 158: 684–697.
Ladybug Tools (2022). Honeybee. Available at https://Github.Com/Ladybug-Tools/Honeybee. Accessed 30 Dec 2022.
Li J, Zhang C, Zhao Y, et al. (2022). Federated learning-based short-term building energy consumption prediction method for solving the data silos problem. Building Simulation, 15: 1145–1159.
National Development and Reform Commission of China (2019). Green and Efficient Refrigeration Action Plan. (in Chinese)
National Renewable Energy Laboratory (2022). OpenStudio-Standards. Available at https://Github.Com/NREL/Openstudio-Standards. Accessed 29 Dec 2022.
Nizam RS, Zhang C, Tian L (2018). A BIM based tool for assessing embodied energy for buildings. Energy and Buildings, 170: 1–14.
Saidur R (2009). Energy consumption, energy savings, and emission analysis in Malaysian office buildings. Energy Policy, 37: 4104–4113.
Wang H, Xu P, Sha H, et al. (2022). BIM-based automated design for HVAC system of office buildings—An experimental study. Building Simulation, 15: 1177–1192.
Yan D, O’Brien W, Hong T, et al. (2015). Occupant behavior modeling for building performance simulation: current state and future challenges. Energy and Buildings, 107: 264–278.
Yang Y, Pan Y, Zeng F, et al. (2022). A gbXML reconstruction workflow and tool development to improve the geometric interoperability between BIM and BEM. Buildings, 12: 221.
Yang J, Deng Z, Guo S, et al. (2023). Development of bottom-up model to estimate dynamic carbon emission for city-scale buildings. Applied Energy, 331: 120410.
Ying H, Lee S (2021). A rule-based system to automatically validate IFC second-level space boundaries for building energy analysis. Automation in Construction, 127: 103724.
Zhang X, Li Z, Li Z, et al. (2022). Differential pressure reset strategy based on reinforcement learning for chilled water systems. Building Simulation, 15: 233–248.
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This work was supported by the National Natural Science Foundation of China (No. 51908204).
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Zhihua Chen: conceptualization, writing—original draft, methodology, investigation, software. Zhang Deng: data curation, software. Adrian Chong: writing—review & editing, supervision. Yixing Chen: conceptualization, software, writing—review & editing, supervision.
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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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Chen, Z., Deng, Z., Chong, A. et al. AutoBPS-BIM: A toolkit to transfer BIM to BEM for load calculation and chiller design optimization. Build. Simul. 16, 1287–1298 (2023). https://doi.org/10.1007/s12273-023-1006-4
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DOI: https://doi.org/10.1007/s12273-023-1006-4