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Dong, Y.; Xu, A.; Zhang, R.; Yu, J. Commercial Complex Atrium Roof with Resilient Ventilation. Encyclopedia. Available online: https://encyclopedia.pub/entry/49529 (accessed on 09 July 2024).
Dong Y, Xu A, Zhang R, Yu J. Commercial Complex Atrium Roof with Resilient Ventilation. Encyclopedia. Available at: https://encyclopedia.pub/entry/49529. Accessed July 09, 2024.
Dong, Yu, Ao Xu, Ruinan Zhang, Jiahui Yu. "Commercial Complex Atrium Roof with Resilient Ventilation" Encyclopedia, https://encyclopedia.pub/entry/49529 (accessed July 09, 2024).
Dong, Y., Xu, A., Zhang, R., & Yu, J. (2023, September 22). Commercial Complex Atrium Roof with Resilient Ventilation. In Encyclopedia. https://encyclopedia.pub/entry/49529
Dong, Yu, et al. "Commercial Complex Atrium Roof with Resilient Ventilation." Encyclopedia. Web. 22 September, 2023.
Commercial Complex Atrium Roof with Resilient Ventilation
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This entry is adapted from the peer-reviewed paper 10.3390/smartcities6050108

Carbon-neutral architectural design focuses on rationally utilizing the building’s surroundings to reduce its environmental impact. Resilient ventilation systems, developed according to the thermal comfort requirements of building energy-saving research, have few applications.

commercial complex atrium energy-saving renovation design resilient ventilation

1. Introduction

Global population growth and the continuous lack of resources resulting from events such as energy shortages and climate warming have caused countries to focus more on international energy trends and changes [1][2][3]. Various countries have proposed energy policies [4][5][6][7]. Performing energy consumption analysis and establishing energy-saving designs are among the most basic technical means of achieving sustainable development. Research examining energy efficiency and the exploration of energy-saving practices has become the focus of extensive attention in both academic and industrial circles [8].
As a typical public building type in cities, commercial complexes possess complex characteristics such as long operation cycles, high pedestrian flows, large spatial spans, and significant urban node effects, all of which lead to huge energy consumption problems with regard to lighting, heating, and cooling operations. Due to the large area stock, fast growth, and lack of energy-saving design awareness of previously completed projects, large-scale commercial complexes exhibit high renovation potential [9][10][11]. Currently, there are sufficient studies focused on passive energy savings in buildings. Architects have reduced building energy consumption by designing building forms, functional layouts, materials, lighting, and other processes [12][13][14]. However, the effective prediction of building performance requires numerous simulations and evaluations, and thousands of alternatives must be screened during the optimization process [15]. Previous studies examining passive strategies have primarily focused on the optimization of enclosed structures such as the window–wall ratio and thermal properties of materials [16][17][18].

2. Energy-Saving Renovation of Existing Commercial Complexes

Passive architectural design refers to improving and creating a thermal and comfortable indoor environment from the perspective of architectural design without relying on construction equipment. Its principle is the application of natural ventilation, natural lighting, temperature and humidity changes, and other parameters, and the purpose is to minimize the consumption of conventional energy. Aksamija discussed the feasibility of achieving net-zero energy consumption targets in commercial building retrofitting by integrating passive design strategies and energy-efficient building systems to improve building performance and reduce energy consumption [14]. Fan et al. focused on the high energy consumption of the atrium in cold areas and analyzed the influence of the proportion, height, layout, and other parameters on energy consumption through an energy consumption simulation. The results revealed that the unit energy consumption increased with an increase in the atrium volume. The surrounding atrium is more energy efficient than the other types. With an improvement in the uniformity of the atrium plane, the total energy consumption of the building increases [19]. Additionally, many scholars have discussed the design, parameters, and materials of building maintenance structures under energy-conservation guidance [20][21][22]. With the maturity of the parametric design platform and the theory and technology of building energy consumption simulations, there have been sufficient studies examining the in-depth quantitative analysis of building energy consumption [23][24]. Amasyali et al. used EnergyPlus and machine learning to understand and improve user behavior and achieved both energy consumption reduction and occupant comfort [25]. Corbin et al. studied a model-predictive control (MPC) environment. The environment integrated MATLAB and EnergyPlus to predict the optimal building control strategy, and this resulted in energy savings of up to 54% compared to the base case and improved thermal comfort for users [26]. Sangireddy and Bhatia et al. endeavored to reduce the amount of computation needed to calculate the energy consumption of different modes and parameter combinations, and they learned from the data generated by simulation and established a support vector machine to achieve the minimum hourly cooling energy consumption level while maintaining the thermal comfort of the residence [27]. The energy consumption of a commercial complex atrium can surge. Previous studies have conducted research examining the layout, structural design, shading, and other parameters. They have also confirmed the feasibility of passive energy-saving design; in spite of this, passive energy-saving studies focused on building spatial forms remain insufficient. Due to the “chimney effect” of the tall atrium of the commercial complex, it has a good natural ventilation capacity. The design of other types of public buildings such as office, cultural, and sports buildings attaches great importance to the natural ventilation efficiency of the atrium spaces. Therefore, it is necessary to consider natural ventilation and energy savings at the beginning of the design of commercial complexes that experience dense human flow and long operational cycles.

3. Thermal Comfort Simulation

Certain researchers calculated indoor thermal comfort using EnergyPlus but ignored the impact of indoor wind speed and temperature [15]. Aghamolaei pointed out that most existing studies have not investigated the joint effect of solar radiation and local wind speed simultaneously. When the model focused on wind, the solar calculation was simplified and vice versa. Therefore, CFD and EnergyPlus should be combined in the research process to improve the accuracy of thermal comfort simulation. This study proposes a novel simulation framework for outdoor environments using CFD and Building Energy Simulation (BES) methods to couple radiation and convective fluxes in outdoor environments [28]. Guo et al. combined EnergyPlus and CFD to evaluate the impacts of climatic conditions and building forms on the natural ventilation of sports venues in subtropical areas [29]. Recently, there has been a gradual increase in the use of CFD software platforms to simulate natural ventilation and improve indoor thermal comfort. However, research combining CFD and EnergyPlus dual-platform simulations to improve resilient ventilation, reduce the frequency of indoor air conditioning, and save energy remains in its infancy. Resilient ventilation has been confirmed to exert a significant impact on the indoor and outdoor thermal comfort of buildings; however, there is still a lack of sufficient quantitative analysis of the effect and duration of different building forms on thermal comfort.

4. Machine Learning and Genetic Algorithm in the Field of Building Energy Conservation

Machine learning (ML) is widely used in the context of data processing and model training, has been relatively well studied, and is a popular topic in the academic field [30][31][32]. Olu-Ajayi pointed out that ML methods are considered the best way to produce the desired results in prediction tasks and have been applied to the field of energy consumption in operational buildings. However, few studies have investigated the applicability of ML methods to predict potential building energy consumption in the early design phase to reduce the construction of low-energy buildings [33]. Zhang revealed the relationship between building shape and thermal comfort performance of semi-outdoor sports venues, selected canopy elevation, façade porosity coefficient, and sun-shade tilt angle as parameters to control building shape and developed a method to optimize building shape using an ANN and a genetic algorithm (GA) [34]. However, a relationship between the building form and energy consumption has not been proposed. Although certain scholars have applied machine learning to explore the relationship between spatial form and architectural performance, existing research is relatively weak. In particular, the relationship between the roof design calculation method and building energy consumption requires further investigation. Low-energy buildings have received considerable attention due to energy conservation policies in various countries worldwide. However, a proper assessment of building energy consumption requires dynamic simulations and the analysis of multiple environments or proposals to obtain the optimal solution. Longo et al. reviewed the optimal design of low-energy buildings and determined that multi-objective optimization and GAs were the most popular [35]. Malatji proposed a multi-objective optimization model and solved it using a GA. In the building energy renovation process, an optimal decision is reached by selecting optimal measures [36]. Moreover, the ANN training model method is mature, and a number of scholars have used artificial neural networks and GAs to improve the computational efficiency and accuracy of time-consuming simulations [37][38]. In the field of computational design, a method of combining machine learning with a GA has long been established, and many scholars have conducted in-depth research and discussions. This is a widely accepted industrial workflow. However, the application of artificial neural network learning and optimization screening to explore the atrium roof shape and building energy consumption must still be further investigated to save simulation time and facilitate architects with regard to adjusting the shape design in the early stage of scheme planning.

References

  1. Tian, W.; Song, J.; Li, Z.; de Wilde, P. Bootstrap techniques for sensitivity analysis and model selection in building thermal performance analysis. Appl. Energy 2014, 135, 320–328.
  2. Niemela, T.; Kosonen, R.; Jokisalo, J. Energy performance and environmental impact analysis of cost-optimal renovation solutions of large panel apartment buildingsin Finland. Sustain. Cities Soc. 2017, 32, 9–30.
  3. Cao, X.; Dai, X.; Liu, J. Building energy-consumption status worldwide and the state-of-the-art technologies for zero-energy buildings during the past decade. Energy Build. 2016, 128, 198–213.
  4. Economidou, M.; Todeschi, V.; Bertoldi, P.; D’Agostino, D.; Zangheri, P.; Castellazzi, L. Review of 50 years of EU energy efficiency policies for buildings. Energy Build. 2020, 225, 110322.
  5. Li, D.H.; Yang, L.; Lam, J.C. Impact of climate change on energy use in the built environment in different climate zones—A review. Energy 2012, 42, 103–112.
  6. Bertoldi, P.; Mosconi, R. Do energy efficiency policies save energy? A new approach based on energy policy indicators (in the EU Member States). Energy Policy 2020, 139, 111320.
  7. Zhao, X.; Li, H.; Wu, L.; Qi, Y. Implementation of energy-saving policies in China: How local governments assisted industrial enterprises in achieving energy-saving targets. Energy Policy 2014, 66, 170–184.
  8. Ferrara, M.; Monetti, V.; Fabrizio, E. Cost-Optimal Analysis for Nearly Zero Energy Buildings Design and Optimization: A Critical Review. Energies 2018, 11, 1478.
  9. Bhandari, M.; Hun, D.; Shrestha, S.; Pallin, S.; Lapsa, M. A Simplified Methodology to Estimate Energy Savings in Commercial Buildings from Improvements in Airtightness. Energies 2018, 11, 3322.
  10. Huang, H.; Chen, L.; Hu, E. A new model predictive control scheme for energy and cost savings in commercial buildings: An airport terminal building case study. Build. Environ. 2015, 89, 203–216.
  11. Li, X.; Malkawi, A. Multi-objective optimization for thermal mass model predictive control in small and medium size commercial buildings under summer weather conditions. Energy 2016, 112, 1194–1206.
  12. Prieto, A.; Knaack, U.; Auer, T.; Klein, T. Passive cooling & climate responsive facade design Exploring the limits of passive cooling strategies to improve the performance of commercial buildings in warm climates. Energy Build. 2018, 175, 30–47.
  13. Lu, P.; Li, J. Acceptable temperature steps for occupants moving between air-conditioned main space and naturally ventilated transitional space of building. Build. Environ. 2020, 182, 107150.
  14. Aksamija, A. Regenerative design and adaptive reuse of existing commercial buildings for net-zero energy use. Sustain. Cities Soc. 2016, 27, 185–195.
  15. Yue, N.; Li, L.; Morandi, A.; Zhao, Y. A metamodel-based multi-objective optimization method to balance thermal comfort and energy efficiency in a campus gymnasium. Energy Build. 2021, 253, 111513.
  16. Shekar, V.; Krarti, M. Control strategies for dynamic insulation materials applied to commercial buildings. Energy Build. 2017, 154, 305–320.
  17. Brunoro, S. Sustainable technologies in the refurbishment of existing building envelopes in Italy. In Proceedings of the International Conference on Sustainable Construction, Materials and Practices, Lisbon, Portugal, 11–13 June 2007; p. 257.
  18. Tavakolan, M.; Mostafazadeh, F.; Eirdmousa, S.J.; Safari, A.; Mirzaei, K. A parallel computing simulation-based multi-objective optimization framework for economic analysis of building energy retrofit: A case study in Iran. J. Build. Eng. 2022, 45, 103485.
  19. Fan, Z.; Zhang, Y. Numerical Investigation of key design parameters impact on energy consumption of commercial complex distributed atrium in cold area of China. In Proceedings of the 3rd International Conference of Green Buildings and Environmental Management (GBEM), Electr Network, Qingdao, China, 5–7 June 2020; Volume 531, p. 2020.
  20. Negendahl, K.; Nielsen, T.R. Building energy optimization in the early design stages: A simplified method. Energy Build. 2015, 105, 88–99.
  21. Yu, J.; Tian, L.; Xu, X.; Wang, J. Evaluation on energy and thermal performance for office building envelope in different climate zones of China. Energy Build. 2015, 86, 626–639.
  22. Zhang, L.; Qin, Y. Case study and countermeasures on commercial building energy saving renovation. In Proceedings of the International Conference on Manufacture Engineering and Environment Engineering (MEEE), Hong Kong, China; 2014; Volume 84, pp. 879–884.
  23. Brown, N.C.; Mueller, C.T. Design for structural and energy performance of long span buildings using geometric multi-objective optimization. Energy Build. 2016, 127, 748–761.
  24. Hong, T.; Luo, X. Modeling Building Energy Performance IN Urban Context. In Proceedings of the Building Performance Analysis Conference and SimBuild, Chicago, LA, USA, 26–28 September 2018; pp. 100–106.
  25. Amasyali, K.; El-Gohary, N. Machine learning for occupant-behavior-sensitive cooling energy consumption prediction in office buildings. Renew. Sustain. Energy Rev. 2021, 142, 110714.
  26. Corbin, C.D.; Henze, G.P.; May-Ostendorp, P. A model predictive control optimization environment for real-time commercial building application. J. Build. Perform. Simul. 2013, 6, 159–174.
  27. Sangireddy, S.A.R.; Bhatia, A.; Garg, V. Development of a surrogate model by extracting top characteristic feature vectors for building energy prediction. J. Build. Eng. 2019, 23, 38–52.
  28. Aghamolaei, R.; Fallahpour, M.; Mirzaei, P.A. Tempo-spatial thermal comfort analysis of urban heat island with coupling of CFD and building energy simulation. Energy Build. 2021, 251, 111317.
  29. Guo, W.; Liang, S.; He, Y.; Li, W.; Xiong, B.; Wen, H. Combining EnergyPlus and CFD to predict and optimize the passive ventilation mode of medium-sized gymnasium in subtropical regions. Build. Environ. 2022, 207, 108420.
  30. Pan, Y.; Zhang, L. Roles of artificial intelligence in construction engineering and management: A critical review and future trends. Autom. Constr. 2021, 122, 103517.
  31. Akinosho, T.D.; Oyedele, L.O.; Bilal, M.; Ajayi, A.O.; Delgado, M.D.; Akinade, O.O.; Ahmed, A.A. Deep learning in the construction industry: A review of present status and future innovations. J. Build. Eng. 2020, 32, 101827.
  32. Khallaf, R.; Khallaf, M. Classification and analysis of deep learning applications in construction: A systematic literature review. Autom. Constr. 2021, 129, 103760.
  33. Olu-Ajayi, R.; Alaka, H.; Sulaimon, I.; Sunmola, F.; Ajayi, S. Building energy consumption prediction for residential buildings using deep learning and other machine learning techniques. J. Build. Eng. 2022, 45, 103406.
  34. Zhang, R.; Liu, D.; Shi, L. Thermal-comfort optimization design method for semi-outdoor stadium using machine learning. Build. Environ. 2022, 215, 108890.
  35. Longo, S.; Montana, F.; Sanseverino, E.R. A review on optimization and cost-optimal methodologies in low-energy buildings design and environmental considerations. Sustain. Cities Soc. 2019, 45, 87–104.
  36. Malatji, E.M.; Zhang, J.; Xia, X. A multiple objective optimisation model for building energy efficiency investment decision. Energy Build. 2013, 61, 81–87.
  37. Lu, Y.; Gong, X.; Kipnis, A.B. Prediction of Low-Energy Building Energy Consumption Based on Genetic BP Algorithm. Comput. Mater. Contin. 2022, 72, 5481–5497.
  38. Ilbeigi, M.; Ghomeishi, M.; Dehghanbanadaki, A. Prediction and optimization of energy consumption in an office building using artificial neural network and a genetic algorithm. Sustain. Cities Soc. 2020, 61, 102325.
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Subjects: Construction & Building Technology
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Yu Dong
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Ao Xu
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Jiahui Yu
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Update Date: 25 Sep 2023
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