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Intelligent multiobjective optimization design for NZEBs in China: Four climatic regions

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  • Wu, Xianguo
  • Li, Xinyi
  • Qin, Yawei
  • Xu, Wen
  • Liu, Yang

Abstract

Near-zero-energy-consumption buildings (NZEBs) are of great significance for sustainable development, and their design and research have attracted increasing academic attention. To drive and realize the energy saving design and achieve carbon emission and thermal comfort optimization of NZEBs, in this paper, an intelligent optimization method integrating the BIM-DB and PSO-RF-NSGA-III method is established. Multiobjective optimization problems involving NZEBs in four typical climate regions in China are explored. With a typical office building as an example, first, simulation calculations regarding the energy consumption, carbon emissions and indoor thermal comfort in the four climatic regions are performed based on orthogonal tests and BIM-DB. Second, the nonlinear mapping relationships between building design parameters and prediction targets are constructed with the PSO-RF model, which is trained with sample data. The obtained nonlinear mapping relations are used to establish the objective function of NSGA-III, and the multiobjective Pareto-optimal solution set is obtained with the developed PSO-RF-NSGA-III algorithm. Finally, the only optimal solution is determined by using the ideal point method, and reference near-zero-energy-consumption office building parameters are calculated for different climate regions. The conclusions are as follows. (1) The PSO-RF algorithm can efficiently predict building energy consumption, carbon emissions and thermal comfort. In the four regions, the goodness of fit of the three targets is greater than 0.94. (2) Multiobjective optimization can be performed with the proposed RF-NSGA-III intelligent optimization method. After optimizing multiple groups of optimization schemes and adopting energy saving measures, the energy consumption levels in the four climate regions are reduced by 39.72 %, 32.22 %, 26.94 % and 35.37 %, and the other goals are optimized. (3) Index calculations indicate that the optimized building design parameters meet the specified standards for NZEBs, and the main influencing factors and corresponding measures vary from region to region.

Suggested Citation

  • Wu, Xianguo & Li, Xinyi & Qin, Yawei & Xu, Wen & Liu, Yang, 2023. "Intelligent multiobjective optimization design for NZEBs in China: Four climatic regions," Applied Energy, Elsevier, vol. 339(C).
  • Handle: RePEc:eee:appene:v:339:y:2023:i:c:s0306261923002982
    DOI: 10.1016/j.apenergy.2023.120934
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    as
    1. Tian, Wei & Heo, Yeonsook & de Wilde, Pieter & Li, Zhanyong & Yan, Da & Park, Cheol Soo & Feng, Xiaohang & Augenbroe, Godfried, 2018. "A review of uncertainty analysis in building energy assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 93(C), pages 285-301.
    2. Assouline, Dan & Mohajeri, Nahid & Scartezzini, Jean-Louis, 2018. "Large-scale rooftop solar photovoltaic technical potential estimation using Random Forests," Applied Energy, Elsevier, vol. 217(C), pages 189-211.
    3. Abokersh, Mohamed Hany & Spiekman, Marleen & Vijlbrief, Olav & van Goch, T.A.J. & Vallès, Manel & Boer, Dieter, 2021. "A real-time diagnostic tool for evaluating the thermal performance of nearly zero energy buildings," Applied Energy, Elsevier, vol. 281(C).
    4. Govindan, K. & Jafarian, A. & Khodaverdi, R. & Devika, K., 2014. "Two-echelon multiple-vehicle location–routing problem with time windows for optimization of sustainable supply chain network of perishable food," International Journal of Production Economics, Elsevier, vol. 152(C), pages 9-28.
    5. Cho, Hyun Mi & Yun, Beom Yeol & Yang, Sungwoong & Wi, Seunghwan & Chang, Seong Jin & Kim, Sumin, 2020. "Optimal energy retrofit plan for conservation and sustainable use of historic campus building: Case of cultural property building," Applied Energy, Elsevier, vol. 275(C).
    6. Ihara, Takeshi & Gustavsen, Arild & Jelle, Bjørn Petter, 2015. "Effect of facade components on energy efficiency in office buildings," Applied Energy, Elsevier, vol. 158(C), pages 422-432.
    7. Suwal, Naresh & Huang, Xianfeng & Kuriqi, Alban & Chen, Yingqin & Pandey, Kamal Prasad & Bhattarai, Khem Prasad, 2020. "Optimisation of cascade reservoir operation considering environmental flows for different environmental management classes," Renewable Energy, Elsevier, vol. 158(C), pages 453-464.
    8. AL-Musaylh, Mohanad S. & Deo, Ravinesh C. & Li, Yan & Adamowski, Jan F., 2018. "Two-phase particle swarm optimized-support vector regression hybrid model integrated with improved empirical mode decomposition with adaptive noise for multiple-horizon electricity demand forecasting," Applied Energy, Elsevier, vol. 217(C), pages 422-439.
    9. Ganjehkaviri, A. & Mohd Jaafar, M.N. & Hosseini, S.E. & Barzegaravval, H., 2017. "Genetic algorithm for optimization of energy systems: Solution uniqueness, accuracy, Pareto convergence and dimension reduction," Energy, Elsevier, vol. 119(C), pages 167-177.
    10. Wen, Yifan & Wu, Ruoxi & Zhou, Zihang & Zhang, Shaojun & Yang, Shengge & Wallington, Timothy J. & Shen, Wei & Tan, Qinwen & Deng, Ye & Wu, Ye, 2022. "A data-driven method of traffic emissions mapping with land use random forest models," Applied Energy, Elsevier, vol. 305(C).
    11. D'Agostino, Delia & Parker, Danny, 2018. "A framework for the cost-optimal design of nearly zero energy buildings (NZEBs) in representative climates across Europe," Energy, Elsevier, vol. 149(C), pages 814-829.
    12. Francisco, Abigail & Truong, Hanh & Khosrowpour, Ardalan & Taylor, John E. & Mohammadi, Neda, 2018. "Occupant perceptions of building information model-based energy visualizations in eco-feedback systems," Applied Energy, Elsevier, vol. 221(C), pages 220-228.
    13. Chaudhuri, Tanaya & Soh, Yeng Chai & Li, Hua & Xie, Lihua, 2019. "A feedforward neural network based indoor-climate control framework for thermal comfort and energy saving in buildings," Applied Energy, Elsevier, vol. 248(C), pages 44-53.
    14. Karali, Nihan & Shah, Nihar & Park, Won Young & Khanna, Nina & Ding, Chao & Lin, Jiang & Zhou, Nan, 2020. "Improving the energy efficiency of room air conditioners in China: Costs and benefits," Applied Energy, Elsevier, vol. 258(C).
    15. Neves, Rebecca & Cho, Heejin & Zhang, Jian, 2021. "Pairing geothermal technology and solar photovoltaics for net-zero energy homes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 140(C).
    16. Kunwar, Niraj & Cetin, Kristen S. & Passe, Ulrike & Zhou, Xiaohui & Li, Yunhua, 2020. "Energy savings and daylighting evaluation of dynamic venetian blinds and lighting through full-scale experimental testing," Energy, Elsevier, vol. 197(C).
    17. Harmathy, Norbert & Magyar, Zoltán & Folić, Radomir, 2016. "Multi-criterion optimization of building envelope in the function of indoor illumination quality towards overall energy performance improvement," Energy, Elsevier, vol. 114(C), pages 302-317.
    18. Zhang, Liang & Wen, Jin & Li, Yanfei & Chen, Jianli & Ye, Yunyang & Fu, Yangyang & Livingood, William, 2021. "A review of machine learning in building load prediction," Applied Energy, Elsevier, vol. 285(C).
    19. Zhou, Zhihua & Zhang, Zhiming & Chen, Guanyi & Zuo, Jian & Xu, Pan & Meng, Chong & Yu, Zhun, 2016. "Feasibility of ground coupled heat pumps in office buildings: A China study," Applied Energy, Elsevier, vol. 162(C), pages 266-277.
    20. Zhu Liu & Dabo Guan & Wei Wei & Steven J. Davis & Philippe Ciais & Jin Bai & Shushi Peng & Qiang Zhang & Klaus Hubacek & Gregg Marland & Robert J. Andres & Douglas Crawford-Brown & Jintai Lin & Hongya, 2015. "Reduced carbon emission estimates from fossil fuel combustion and cement production in China," Nature, Nature, vol. 524(7565), pages 335-338, August.
    21. Guo, Yabin & Wang, Jiangyu & Chen, Huanxin & Li, Guannan & Liu, Jiangyan & Xu, Chengliang & Huang, Ronggeng & Huang, Yao, 2018. "Machine learning-based thermal response time ahead energy demand prediction for building heating systems," Applied Energy, Elsevier, vol. 221(C), pages 16-27.
    22. Wang, Yihan & Chen, Chen & Tao, Yuan & Wen, Zongguo & Chen, Bin & Zhang, Hong, 2019. "A many-objective optimization of industrial environmental management using NSGA-III: A case of China’s iron and steel industry," Applied Energy, Elsevier, vol. 242(C), pages 46-56.
    23. Zhong, Hai & Wang, Jiajun & Jia, Hongjie & Mu, Yunfei & Lv, Shilei, 2019. "Vector field-based support vector regression for building energy consumption prediction," Applied Energy, Elsevier, vol. 242(C), pages 403-414.
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    2. Zikang Ke & Xiaoxin Liu & Hui Zhang & Xueying Jia & Wei Zeng & Junle Yan & Hao Hu & Wong Nyuk Hien, 2023. "Energy Consumption and Carbon Emissions of Nearly Zero-Energy Buildings in Hot Summer and Cold Winter Zones of China," Sustainability, MDPI, vol. 15(14), pages 1-20, July.
    3. Baidi Shi & Liangxian Zhang & Yongfeng Jiang & Zixing Li & Wei Xiao & Jingyu Shang & Xinfu Chen & Meng Li, 2023. "Three-Phase Transformer Optimization Based on the Multi-Objective Particle Swarm Optimization and Non-Dominated Sorting Genetic Algorithm-3 Hybrid Algorithm," Energies, MDPI, vol. 16(22), pages 1-21, November.

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