Building retrofit multiobjective optimization using neural networks and genetic algorithm three for energy carbon and comfort.
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| Názov: | Building retrofit multiobjective optimization using neural networks and genetic algorithm three for energy carbon and comfort. |
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| Autori: | Duan Z; School of Architecture and Design, China University of Mining and Technology, Jiangsu, China., Li B; School of Architecture and Design, China University of Mining and Technology, Jiangsu, China., Zi Y; School of Architecture and Design, China University of Mining and Technology, Jiangsu, China., Yao G; School of Architecture and Design, China University of Mining and Technology, Jiangsu, China. yaogang110@cumt.edu.cn. |
| Zdroj: | Scientific reports [Sci Rep] 2025 Oct 30; Vol. 15 (1), pp. 38076. Date of Electronic Publication: 2025 Oct 30. |
| Spôsob vydávania: | Journal Article |
| Jazyk: | English |
| Informácie o časopise: | Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: PubMed not MEDLINE; MEDLINE |
| Imprint Name(s): | Original Publication: London : Nature Publishing Group, copyright 2011- |
| Abstrakt: | In response to the urgent need for energy-efficient retrofits in industrial buildings under global climate goals, this study presents a robust multi-objective optimization framework that integrates building performance simulation, surrogate modeling, evolutionary algorithms, and decision analysis. A representative old factory building in Wenzhou, China, is selected as the case study. DesignBuilder is used to simulate energy consumption, thermal comfort, and carbon emissions. To reduce computational costs, surrogate models based on Backpropagation Neural Networks (BPNN) and Support Vector Regression (SVR) are developed and compared in terms of predictive performance.The results show that BPNN demonstrates superior predictive accuracy compared to SVR, with higher R and lower RMSE values. Then, the Non-dominated Sorting Genetic Algorithm III (NSGA-III) is employed to generate a set of Pareto-optimal solutions, and the entropy-weighted TOPSIS method is applied to identify the most balanced retrofit option. The optimized design results in a 10.06% reduction in thermal discomfort hours (Tdh), a 35.45% reduction in energy density index (EDI), and a 28.86% reduction in life-cycle carbon emissions (LCCO₂), respectively. Overall, the proposed framework proves to be highly applicable to the low-carbon renovation of existing industrial buildings, offering a practical and scalable decision-support approach for achieving a balance among energy efficiency, environmental sustainability, and indoor comfort. (© 2025. The Author(s).) |
| Competing Interests: | Declarations. Competing interests: 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. |
| References: | Research Report on Energy Consumption and Carbon Emissions of Buildings in China. Architecture 46–59 (2023). Kamazani, M. A. & Dixit, M. K. Multi-objective optimization of embodied and operational energy and carbon emission of a Building envelope. J. Clean. Prod. 428, 139510 (2023). (PMID: 10.1016/j.jclepro.2023.139510) Chen, R. An integrated framework for multi-objective optimization of building performance: carbon emissions, thermal comfort, and global cost. J. Clean. Prod. 359, 131978 (2022). Ciacci, C. & Bazzocchi, F. Retrofitting of the Italian precast industrial Building stock. LCA analysis as decision-making tool. J. Clean. Prod. 478, 143933 (2024). (PMID: 10.1016/j.jclepro.2024.143933) Ciampi, F. G. et al. Energy consumption prediction of industrial HVAC systems using bayesian networks. Energy Build. 309, 114039 (2024). (PMID: 10.1016/j.enbuild.2024.114039) Zhou, B. & Wang, D. Integrated performance optimization of industrial buildings in relation to thermal comfort and energy consumption: a case study in hot summer and cold winter climate. Case Stud. Therm. Eng. 46, 102991 (2023). (PMID: 10.1016/j.csite.2023.102991) Machairas, V., Tsangrassoulis, A. & Axarli, K. Algorithms for optimization of building design: a review. Renew. Sustain. Energy Rev. 31, 101–112 (2014). (PMID: 10.1016/j.rser.2013.11.036) Choi, J. H. Investigation of the correlation of Building energy use intensity estimated by six building performance simulation tools. Energy Build. 147, 14–26 (2017). Zhu, L. Research on the Energy-saving scheme of an electronic factory building. Southwest Jiaotong University. https://doi.org/10.27414/d.cnki.gxnju.2021.003806 (2021). Ascione, F. et al. A real industrial building: modeling, calibration and Pareto optimization of energy retrofit. J. Build. Eng. 29, 101186 (2020). (PMID: 10.1016/j.jobe.2020.101186) Shi, X. A review on Building energy efficient design optimization Rom the perspective of architects. Renew. Sustain. Energy Rev. 65, 872–884 (2016). Liu, R. Controllable cross-building multi-objective optimisation for nzebs: a framework utilising parametric generation and intelligent algorithms. Appl. Energy 374, 124003 (2024). Bre, F., Roman, N. & Fachinotti, V. D. An efficient metamodel-based method to carry out multi-objective Building performance optimizations. Energy Build. 206, 109576 (2020). (PMID: 10.1016/j.enbuild.2019.109576) Zhang, Z., Wang, W., Song, J., Wang, Z. & Wang, W. Multi-objective optimization of ultra-low energy consumption buildings in severely cold regions considering life cycle performance. Sustainability 14, 16440 (2022). (PMID: 10.3390/su142416440) Hosamo, H. H., Tingstveit, M. S., Nielsen, H. K., Svennevig, P. R. & Svidt, K. Multiobjective optimization of building energy consumption and thermal comfort based on integrated BIM framework with machine learning-NSGA II. Energy Build. 277, 112479 (2022). (PMID: 10.1016/j.enbuild.2022.112479) Guo, H. et al. Machine learning-based method for detached energy-saving residential form generation. Buildings 12, 1504 (2022). (PMID: 10.3390/buildings12101504) Hamdy, M., Nguyen, A. T. & Hensen, J. L. M. A performance comparison of multi-objective optimization algorithms for solving nearly-zero-energy-building design problems. Energy Build. 121, 57–71 (2016). (PMID: 10.1016/j.enbuild.2016.03.035) Nguyen, A. T., Reiter, S. & Rigo, P. A review on simulation-based optimization methods applied to Building performance analysis. Appl. Energy. 113, 1043–1058 (2014). (PMID: 10.1016/j.apenergy.2013.08.061) Bagheri-Esfeh, H. & Dehghan, M. R. Multi-objective optimization of setpoint temperature of thermostats in residential buildings. Energy Build. 261, 111955 (2022). (PMID: 10.1016/j.enbuild.2022.111955) Liu, F., Ouyang, T., Huang, B. & Zhao, J. Research on green building design optimization based on building information modeling and improved genetic algorithm. Adv. Civ. Eng. 2024, 9786711 (2024). Vera, S. Optimization of a fixed exterior complex fenestration system considering visual comfort and energy performance criteria. Build. Environ. 113, 163–174 (2017). Xu, Y. et al. A two-stage multi-objective optimization method for envelope and energy generation systems of primary and secondary school teaching buildings in China. Build. Environ. 204, 108142 (2021). (PMID: 10.1016/j.buildenv.2021.108142) Li, K., Pan, L., Xue, W., Jiang, H. & Mao, H. Multi-objective optimization for energy performance improvement of residential buildings: a comparative study. Energies 10, 245 (2017). (PMID: 10.3390/en10020245) Razmi, A., Rahbar, M. & Bemanian, M. PCA-ANN integrated NSGA-III framework for dormitory Building design optimization: energy efficiency, daylight, and thermal comfort. Appl. Energy. 305, 117828 (2022). (PMID: 10.1016/j.apenergy.2021.117828) ABO Issa, M. A. Building Performance Simulation for Architects, Comparing Three Leading Simulation Tools (The University of Texas at San Antonio, United States -- Texas, 2018). Order No. 10686099; ISBN 978-0-355-95900-0. Benaddi, F. Z. Multi-objective optimization of building envelope components based on economic, environmental, and thermal comfort criteria. Energy and Buildings 305, 113909 (2024). Wang, A. Multi-objective Optimization of Building Energy Consumption and Thermal Comfort Based on SVR-NSGA-II. Case Studies in Thermal Engineering 63, 105368 (2024). Zhang, X., Ning, Q. & Chen, Z. Multi-objective optimization design of energy efficiency for office Building window systems based on indoor thermal comfort. Science and Technology for the Built Environment 29, 618–631 (2023). Kang, Y. et al. Integrated passive design method optimized for carbon emissions, economics, and thermal comfort of zero-carbon buildings. Energy 295, 131048 (2024). (PMID: 10.1016/j.energy.2024.131048) Westermann, P. Surrogate modelling for sustainable building design—a review. Energy and Buildings 198, 170–186 (2019). Tian, W. A review of sensitivity analysis methods in building energy analysis. Renew. Sustain. Energy Rev. 20, 411–419 (2013). (PMID: 10.1016/j.rser.2012.12.014) Yang, S. et al. Comparison of sensitivity analysis methods in building energy assessment. Procedia Eng. 146, 174–181 (2016). (PMID: 10.1016/j.proeng.2016.06.369) Hu, Y. et al. Short term electric load forecasting model and its verification for process industrial enterprises based on hybrid GA-PSO-BPNN algorithm—a case study of papermaking process. Energy 170, 1215–1227 (2019). (PMID: 10.1016/j.energy.2018.12.208) Thota, R. & Sinha, N. A novel optimized hybrid machine learning model to enhance the prediction accuracy of hourly Building energy consumption. Energy sources part A-recovery util. Environ. Eff. 46, 9112–9135 (2024). Sui, Z. Evaluation of energy saving of residential buildings in North China using back-propagation neural network and virtual reality modeling. J. Energy Eng. 148, 04022013 (2022). Yu, M., Li, L. & Guo, Z. Model analysis of energy consumption data for green building using deep learning neural network. Int. J. Low-Carbon Technol. 17, 233–244 (2022). (PMID: 10.1093/ijlct/ctab100) Li, C. et al. Optimal design of building envelope towards life cycle performance: impact of considering dynamic grid emission factors. Energy Build. 323, 114770 (2024). (PMID: 10.1016/j.enbuild.2024.114770) Dixit, M. K. Life cycle embodied energy analysis of residential buildings—a review of literature to investigate embodied energy parameters. Renew. Sustain. Energy Rev. 79, 390–413 (2017). Ministry of Housing and Urban-Rural Development of the People’s Republic of China. Standard for Building Carbon Emission Calculation (2019). Cui Peng. Research on the Construction and Application of a Carbon Emission Factor Database for Building Life Cycles. Southeast University (2015). Piasecki, M., Fedorczak-Cisak, M., Furtak, M. & Biskupski, J. Experimental confirmation of the reliability of Fanger’s thermal comfort model—case study of a near-zero energy building (NZEB) office building. Sustainability 11, 9 (2019). Wonyoung, Y. Thermal comfort range of PMV for Koreans by subjective assessment in an indoor environmental chamber. Korean Soc. Living Environ. Syst. 24, 37–45 (2017). (PMID: 10.21086/ksles.2017.02.24.1.37) Martínez-Comesaña, M., Eguía-Oller, P., Martínez-Torres, J., Febrero-Garrido, L. & Granada-Álvarez, E. Optimisation of thermal comfort and indoor air quality estimations applied to in-use buildings combining NSGA-III and XGBoost. Sustain. Cities Soc. 80, 103723 (2022). (PMID: 10.1016/j.scs.2022.103723) Wu, X. et al. Intelligent optimization framework of near zero energy consumption building performance based on a hybrid machine learning algorithm. Renew. Sustain. Energy Rev. 167, 112703 (2022). (PMID: 10.1016/j.rser.2022.112703) Chen, H. & An, Y. Green residential building design scheme optimization based on the orthogonal experiment EWM-TOPSIS. Buildings 14, 452 (2024). (PMID: 10.3390/buildings14020452) Chen, P. Effects of normalization on the entropy-based TOPSIS method. Expert Syst. Appl. 136, 33–41 (2019). Ministry of Housing and Urban-Rural Development of the People’s Republic of China. Uniform standard for energy-saving design of industrial buildings (2017). Ministry of Housing and Urban-Rural Development of the People’s Republic of China. Technical Standard for Nearly Zero Energy Buildings (2019). Xue, Q., Wang, Z. & Chen, Q. Multi-objective optimization of Building design for life cycle cost and CO Xue, B. et al. Research on energy efficiency evaluation model of substation Building based on AHP and fuzzy comprehensive theory. Sustainability 15, 14493 (2023). (PMID: 10.3390/su151914493) Cao, Z. et al. Study on the optimal layout of roof vents and rooftop photovoltaic of the industrial workshop. Build. Environ. 260, 111624 (2024). (PMID: 10.1016/j.buildenv.2024.111624) Shan, R., Lai, W., Tang, H., Leng, X. & Gu, W. Residential building renovation considering energy, carbon emissions, and cost: an approach integrating machine learning and evolutionary generation. Appl. Sci. 15, 1830 (2025). (PMID: 10.3390/app15041830) Shi, Y. & Chen, P. Energy retrofitting of hospital buildings considering climate change: an approach integrating automated machine learning with NSGA-III for multi-objective optimization. Energy Build. 319, 114571 (2024). (PMID: 10.1016/j.enbuild.2024.114571) Kaya, G. N., Beyhan, F., İlerisoy, Z. Y. & Cudzik, J. Evaluation of machine learning applications in building life cycle processes for energy efficiency: a systematic review. Energy Rep. 13, 4900–4916 (2025). (PMID: 10.1016/j.egyr.2025.04.034) |
| Grant Information: | 2024WLJCRCZL338 China University of Mining and Technology; 2025JCXKSK17 the Fundamental Research Funds for the Central Universities |
| Contributed Indexing: | Keywords: Carbon emission; Industrial buildings; Multi-objective optimization; Neural networks; Old factory buildings |
| Entry Date(s): | Date Created: 20251031 Latest Revision: 20251102 |
| Update Code: | 20251102 |
| PubMed Central ID: | PMC12575624 |
| DOI: | 10.1038/s41598-025-21871-0 |
| PMID: | 41168258 |
| Databáza: | MEDLINE |
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Nájsť tento článok vo Web of Science | Abstrakt: | In response to the urgent need for energy-efficient retrofits in industrial buildings under global climate goals, this study presents a robust multi-objective optimization framework that integrates building performance simulation, surrogate modeling, evolutionary algorithms, and decision analysis. A representative old factory building in Wenzhou, China, is selected as the case study. DesignBuilder is used to simulate energy consumption, thermal comfort, and carbon emissions. To reduce computational costs, surrogate models based on Backpropagation Neural Networks (BPNN) and Support Vector Regression (SVR) are developed and compared in terms of predictive performance.The results show that BPNN demonstrates superior predictive accuracy compared to SVR, with higher R and lower RMSE values. Then, the Non-dominated Sorting Genetic Algorithm III (NSGA-III) is employed to generate a set of Pareto-optimal solutions, and the entropy-weighted TOPSIS method is applied to identify the most balanced retrofit option. The optimized design results in a 10.06% reduction in thermal discomfort hours (Tdh), a 35.45% reduction in energy density index (EDI), and a 28.86% reduction in life-cycle carbon emissions (LCCO₂), respectively. Overall, the proposed framework proves to be highly applicable to the low-carbon renovation of existing industrial buildings, offering a practical and scalable decision-support approach for achieving a balance among energy efficiency, environmental sustainability, and indoor comfort.<br /> (© 2025. The Author(s).) |
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| ISSN: | 2045-2322 |
| DOI: | 10.1038/s41598-025-21871-0 |