Multi-Objective Optimization Technology for Building Energy-Saving Renovation Strategy Based on Genetic Algorithm
DOI:
https://doi.org/10.31181/dmame7220241073Keywords:
NSGA-II, Architecture, Energy Saving Design, Multi Objective, Carbon EmissionAbstract
Building energy-saving design is significant for the industry to achieve carbon reduction and sustainable development. Firstly, a multi-objective model for energy consumption, cost, and carbon emissions is established based on the three-dimensional perspectives of society, nature, and economy. Then, a polynomial operator is used to improve the non dominated sorting genetic algorithm to calculate the optimal solution set. The low computational efficiency caused by direct coupling of algorithms in traditional optimization processes is expected to be addressed. According to the results, for the Square1 dataset and Iris dataset, the algorithm proposed in this study improved the reverse distance and convergence metrics by more than 70% compared to support vector machine-genetic algorithm and multi-objective clustering algorithm, with values closer to 0. The solution solved by this algorithm had lower building costs, energy consumption, and carbon emissions, with values of 345200 yuan, 2374 KWh/year, and 26 tons, respectively. This validates the effectiveness of the multi-objective model and solving algorithm established in the study, which helps to obtain the optimal energy-saving design scheme and provides reference for low-carbon optimization of building.
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Kumar, A., Pant, S., Ram, M., & Negi, G. (2022). Multi-Objective Optimization of Complex Bridge Network by MOPSO-CD. In 2022 International Conference on Advances in Computing, Communication and Materials (ICACCM) (pp. 1-6). IEEE. https://doi.org/10.1109/ICACCM56405.2022.10009336
Xue, Q., Wang, Z., & Chen, Q. (2022). Multi-objective optimization of building design for life cycle cost and CO2 emissions: A case study of a low-energy residential building in a severe cold climate. Building simulation. Beijing: Tsinghua University Press, 15(1), 83-98. https://doi.org/10.1007/s12273-021-0796-5
Kumar, A., Pant, S., Ram, M., & Yadav, O. (2022). Meta-heuristic Optimization Techniques: Applications in Engineering. Berlin, Boston: De Gruyter. https://doi.org/10.1515/9783110716214
SS, V. C., & HS, A. (2022). Nature inspired meta heuristic algorithms for optimization problems. Computing, 104(2), 251-269. https://doi.org/10.1007/s00607-021-00955-5
Shen, T., Teng, L., Hu, Y., & Shen, W. (2022). Systematic screening procedure and innovative energy-saving design for ionic liquid-based extractive distillation process. Frontiers of Chemical Science and Engineering, 17(1), 34-45. https://doi.org/10.1007/s11705-022-2234-3
Langevin, J., Harris, C. B., Satre-Meloy, A., Chandra-Putra, H., Speake, A., Present, E., Adhikari, R., Wilson, E. J. H., & Satchwell, A. J. (2021). US building energy efficiency and flexibility as an electric grid resource. Joule, 5(8), 2102-2128. https://doi.org/10.1016/j.joule.2021.06.002
Berawi, M. A., Kim, A. A., Naomi, F., Basten, V., Miraj, P., Medal, L. A., & Sari, M. (2023). Designing a smart integrated workspace to improve building energy efficiency: an Indonesian case study. International Journal of Construction Management, 23(3), 410-422. https://doi.org/10.1080/15623599.2021.1882747
Loengbudnark, W., Khalilpour, K., Bharathy, G., Voinov, A., & Thomas, L. (2023). Impact of occupant autonomy on satisfaction and building energy efficiency. Energy and Built Environment, 4(4), 377-385. https://doi.org/10.1016/j.enbenv.2022.02.007
Copiello, S. (2021). Economic viability of building energy efficiency measures: A review on the discount rate. AIMS Energy, 9(2), 257-285. https://doi.org/10.3934/energy.2021014
Zhong, H., Wang, J., Jia, H., & Huang, L. (2019). Vector field-based support vector regression for building energy consumption prediction. Applied Energy, 242, 403-414. https://doi.org/10.1016/j.apenergy.2019.03.078
Yu, L., Sun, Y., Xu, Z., Shen, C., Yue, D., Jiang, T., & Guan, X. (2020). Multi-agent deep reinforcement learning for HVAC control in commercial buildings. IEEE Transactions on Smart Grid, 12(1), 407-419. https://doi.org/10.1109/TSG.2020.3011739
Satrio, P., Mahlia, T. M. I., Giannetti, N., & Saito, K. (2019). Optimization of HVAC system energy consumption in a building using artificial neural network and multi-objective genetic algorithm. Sustainable Energy Technologies and Assessments, 35, 48-57. https://doi.org/10.1016/j.seta.2019.06.002
Vukadinović, A., Radosavljević, J., Đorđević, A., Protić, M., & Petrović, N. (2021). Multi-objective optimization of energy performance for a detached residential building with a sunspace using the NSGA-II genetic algorithm. Solar Energy, 224, 1426-1444. https://doi.org/10.1016/j.solener.2021.06.082
Zhai, Y., Wang, Y., Huang, Y., & Meng, X. (2019). A multi-objective optimization methodology for window design considering energy consumption, thermal environment and visual performance. Renewable energy, 134, 1190-1199. https://doi.org/10.1016/j.renene.2018.09.024
Zhao, J., & Du, Y. (2020). Multi-objective optimization design for windows and shading configuration considering energy consumption and thermal comfort: A case study for office building in different climatic regions of China. Solar Energy, 206, 997-1017. https://doi.org/10.1016/j.solener.2020.05.090
Kumar, A., Vohra, M., Pant, S., & Singh, S. K. (2021). Optimization techniques for petroleum engineering: A brief review. International Journal of Modelling and Simulation, 41(5), 326-334. https://doi.org/10.1080/02286203.2021.1983074
Chaube, S., Singh, S. B., Pant, S., & Kumar, A. (2018). Time-dependent conflicting bifuzzy set and its applications in reliability evaluation. Advanced mathematical techniques in engineering sciences, 4, 111-128. https://doi.org/10.1201/b22440-6
Kumar, A., Pant, S., Singh, M. K., Chaube, S., Ram, M., & Kumar, A. (2023). Modified wild horse optimizer for constrained system reliability optimization. Axioms, 12(7), 693. https://doi.org/10.3390/axioms12070693
Veasna, K., Feng, Z., Zhang, Q., & Knezevic, M. (2023). Machine learning-based multi-objective optimization for efficient identification of crystal plasticity model parameters. Computer Methods in Applied Mechanics and Engineering, 403, 115740. https://doi.org/10.1016/j.cma.2022.115740
Ji, Y., Yang, Z., Ran, J., & Li, H. (2021). Multi-objective parameter optimization of turbine impeller based on RBF neural network and NSGA-II genetic algorithm. Energy Reports, 7, 584-593. https://doi.org/10.1016/j.egyr.2021.10.003
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