DOI: https://doi.org/10.61435/ijred.2024.60483

Multi-objective decision optimization design for building energy-saving retrofitting design based on improved grasshopper optimization algorithm

1School of Civil Engineering, Jilin Jianzhu University, Changchun, 130119, China

2Jilin Construction Engineering Group Co., Ltd., Changchun, 130000, China

Received: 17 Jul 2024; Revised: 26 Aug 2024; Accepted: 18 Sep 2024; Available online: 30 Sep 2024; Published: 1 Nov 2024.
Editor(s): Grigorios Kyriakopoulos
Open Access Copyright (c) 2024 The Author(s). Published by Centre of Biomass and Renewable Energy (CBIORE)
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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Abstract
With the national emphasis on building energy efficiency planning, energy efficiency optimization in existing buildings requires renovation measures based on multi-objective factors. In order to get the optimal solution in the multi-objective decision-making of renovation, the study proposes a class of improved grasshopper optimization algorithms. The process employs a systematic methodology to identify an optimal energy renovation method, taking into account the specific characteristics of the building environment. It then classifies and formulates the energy reduction substitution items for building renovation, and finally, it synchronizes the cost of the renovation project as a measure for decision-making. The elite inverse strategy approach enhances the grasshopper optimization algorithm to facilitate the multi-objective decision-making process associated with building renovation measures. The results showed that the improved grasshopper optimization algorithm could achieve a decision accuracy of 98.8% for the test samples, which was 5.5% higher than the accuracy of the particle swarm optimization algorithm. Repeated run tests of the research algorithm for multi-objective decision making yielded a mean decision fitness value of 2.34×104 and a data extreme value of 0.38×104. Compared to other algorithms improved grasshopper optimization algorithm converged in a lower range of fitness values, which indicated that the algorithm worked well for multi-objective optimization and the model repeatability was good. The research algorithm was used to decide the energy efficient renovation planning of the building and the power consumption of the renovated power supply system was reduced by 23.7%-49.6%. This indicates that the renovated building has better energy efficiency and can provide a reliable technical direction for decision-making optimization of building energy efficiency renovation.
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Keywords: Building energy efficiency planning; Multi-objective decision making; Meta-heuristic approach; Elite inverse strategy approach; Grasshopper optimization algorithm; New energy sources.

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Section: Original Research Article
Language : EN
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  1. Badr, A. A., Saafan, M. M., Abdelsalam, M. M., & Haikal, A. Y. (2023). Novel variants of grasshopper optimization algorithm to solve numerical problems and demand side management in smart grids. Artificial Intelligence Review, 56(10), 10679-10732. https://doi.org/10.1007/s10462-023-10431-5
  2. Balezentis, T., Streimikiene, D., Stankuniene, G., & Shobande, O. A. (2024). Willingness to pay for climate change mitigation measures in households: Bundling up renewable energy, energy efficiency, and renovation. Sustainable Development, 32(3), 2385-2402. https://doi.org/10.1002/sd.2784
  3. Bhuyan, H. K., Ravi, V., & Yadav, M. S. (2022). Multi-objective optimization-based privacy in data mining. Cluster Computing, 25(6), 4275-4287. https://doi.org/10.1007/s10586-022-03667-3
  4. Brahami, M. A., Dahane, M., Souier, M., & Sahnoun, M. H. (2022). Sustainable capacitated facility location/network design problem: a non-dominated sorting genetic algorithm based multiobjective approach. Annals of Operations Research, 311(2), 821-852. https://doi.org/10.1007/s10479-020-03659-9
  5. Decorte, Y., Van Den Bossche, N., & Steeman, M. (2023). Guidelines for defining the reference study period and system boundaries in comparative LCA of building renovation and reconstruction. The International Journal of Life Cycle Assessment, 28(2), 111-130. https://doi.org/10.1007/s11367-022-02114-0
  6. Deng, L., & Liu, S. (2023). A novel hybrid grasshopper optimization algorithm for numerical and engineering optimization problems. Neural Processing Letters, 55(7), 9851-9905. https://doi.org/10.1007/s11063-023-11230-3
  7. Deng, Z., Chen, Y., Yang, J., & Chen, Z. (2022). Archetype identification and urban building energy modeling for city-scale buildings based on GIS datasets. Building Simulation, 15(9), 1547-1559. https://doi.org/10.1007/s12273-021-0878-4
  8. Elsheikh, A., Motawa, I., & Diab, E. (2023). Multi-objective genetic algorithm optimization model for energy efficiency of residential building envelope under different climatic conditions in Egypt. International Journal of Construction Management, 23(7), 1244-1253. https://doi.org/10.1080/15623599.2021.1966709
  9. Farghali, M., Osman, A. I., Mohamed, I. M. A., Chen, Z., Chen, L., Ihara, I., Yap, P. S., & Rooney, D. W. (2023). Strategies to save energy in the context of the energy crisis: a review. Environmental Chemistry Letters, 21(4), 2003-2039. https://doi.org/10.1007/s10311-023-01591-5
  10. Goyal, N. (2022). Policy diffusion through multiple streams: The (Non-) adoption of energy conservation building code in India. Policy Studies Journal, 50(3), 641-669. https://doi.org/10.1111/psj.12415
  11. Han, T., Liu, P., Niu, C., & Li, Q. (2023). Evaluation of energy-saving retrofit projects of existing rural residential envelope structures from the perspective of rural residents: the Chinese case. Environment, Development and Sustainability, 25(8), 8419-8446. https://doi.org/10.1007/s10668-022-02406-3
  12. Han, T., Liu, P., Niu, C., & Li, Q. (2023). Evaluation of energy-saving retrofit projects of existing rural residential envelope structures from the perspective of rural residents: the Chinese case. Environment, Development and Sustainability, 25(8), 8419-8446. https://doi.org/10.1007/s10668-022-02406-3
  13. Hosseini, S., Monjezi, M., & Bakhtavar, E. (2022). Minimization of blast-induced dust emission using gene-expression programming and grasshopper optimization algorithm: a smart mining solution based on blasting plan optimization. Clean Technologies and Environmental Policy, 24(8), 2313-2328. https://doi.org/10.1007/s10098-022-02327-9
  14. Jowkar, M., Temeljotov-Salaj, A., Lindkvist, C. M., & Støre-Valen, M. (2022). Sustainable building renovation in residential buildings: barriers and potential motivations in Norwegian culture. Construction Management and Economics, 40(3), 161-172. https://doi.org/10.1080/01446193.2022.2027485
  15. Khan, F. A., Ullah, K., Ur Rahman, A., & Anwar, S. (2023). Energy optimization in smart urban buildings using bio-inspired ant colony optimization. Soft Computing, 27(2), 973-989. https://doi.org/10.1007/s00500-022-07537-3
  16. Kiss, B., & Szalay, Z. (2023). Sensitivity of buildings’ carbon footprint to electricity decarbonization: a life cycle–based multi-objective optimization approach. The International Journal of Life Cycle Assessment, 28(7), 933-952. https://doi.org/10.1007/s11367-022-02043-y
  17. Lin, Y., & Yang, W. (2022). Tri-optimization of building shape and envelope properties using Taguchi and constraint limit method. Engineering, Construction and Architectural Management, 29(3), 1284-1306. https://doi.org/10.1108/ecam-05-2020-0327
  18. Liu, X., Ge, Y., Chen, L., Shi, S., & Feng, H. (2024). Multi-objective optimization for an irreversible Braysson cycle. Journal of Thermal Analysis and Calorimetry, 149(8), 3471-3485. https://doi.org/10.1007/s10973-024-12903-4
  19. Liu, Y., Ming, H., Luo, X., Hu, L., & Sun, Y. (2023). Timetabling optimization of classrooms and self-study rooms in university teaching buildings based on the building controls virtual test bed platform considering energy efficiency. Building Simulation, 16(2), 263-277. https://doi.org/10.1007/s12273-022-0938-4
  20. Ma, H., Zhang, Y., Sun, S., Liu, T., & Shan, Y. (2023). A comprehensive survey on NSGA-II for multi-objective optimization and applications. Artificial Intelligence Review, 56(12), 15217-15270. https://doi.org/10.1007/s10462-023-10526-z
  21. Martín-Ortega, J. L., Chornet, J., Sebos, I., Akkermans, S., & López Blancon M. J. (2024). Enhancing transparency of climate efforts: MITICA’s integrated approach to greenhouse gas mitigation. Sustainability, 16(10), 4219. https://doi.org/10.3390/su16104219
  22. Nikas, A., Fountoulakis, A., Forouli, A., & Doukas, H. (2022). A robust augmented ε-constraint method (AUGMECON-R) for finding exact solutions of multi-objective linear programming problems. Operational Research, 22(2), 1291-1332. https://doi.org/10.1007/s12351-020-00574-6
  23. Noorzai, E., Bakmohammadi, P., & Garmaroudi, M. A. (2023). Optimizing daylight, energy and occupant comfort performance of classrooms with photovoltaic integrated vertical shading devices. Architectural Engineering and Design Management, 19(4), 394-418. https://doi.org/10.1080/17452007.2022.2080173
  24. Reddy, K. N., & Bojja, P. (2022). A novel method to solve visual tracking problem: hybrid algorithm of grasshopper optimization algorithm and differential evolution. Evolutionary Intelligence, 15(1), 785-822. https://doi.org/10.1007/s12065-021-00567-0
  25. Ren, W., Zhao, J., & Chang, M. (2023). Energy-saving optimization based on residential building orientation and shape with multifactor coupling in the Tibetan areas of western Sichuan, China. Journal of Asian Architecture and Building Engineering, 22(3), 1476-1491. https://doi.org/10.1080/13467581.2022.2085725
  26. Sharma, S., & Kumar, V. (2022). A comprehensive review on multi-objective optimization techniques: Past, present and future. Archives of Computational Methods in Engineering, 29(7), 5605-5633. https://doi.org/10.1007/s11831-022-09778-9
  27. Srinivasulu, M., Shivamurthy, G., & Venkataramana, B. (2023). Quality of service aware energy efficient multipath routing protocol for internet of things using hybrid optimization algorithm. Multimedia Tools and Applications, 82(17), 26829-26858. https://doi.org/10.1007/s11042-022-14285-x
  28. Usman, A. M., & Abdullah, M. K. (2023). An assessment of building energy consumption characteristics using analytical energy and carbon footprint assessment model. Green and Low-Carbon Economy, 1(1), 28-40. https://doi.org/10.47852/bonviewGLCE3202545
  29. Verma, A., Prakash, S., & Kumar, A. (2023). AI-based building management and information system with multi-agent topology for an energy-efficient building: towards occupants comfort. IETE Journal of Research, 69(2), 1033-1044. https://doi.org/10.1080/03772063.2020.1847701
  30. Vijayan, D. S., Sivasuriyan, A., Patchamuthu, P., & Jayaseelan, R. (2022). Thermal performance of energy-efficient buildings for sustainable development. Environmental Science and Pollution Research, 29(34), 51130-51142. https://doi.org/10.1007/s11356-021-17602-3
  31. Wang, P., Chen, H., Si, Z., Jia, L., Wang, J., Li, K., & Wang, C. (2024). Effectively solve the obstacle in the old residential building energy-saving renovation from the perspective of a four-party evolutionary game. Environmental Science and Pollution Research, 31(6), 9011-9030. https://doi.org/10.1007/s11356-023-31591-5
  32. Wang, Q., Elfeky, K. E., Ma, T., Cheng, Z. L., & Ge, K. (2024). Selected papers from the 6th international workshop on heat/mass transfer advances for energy conservation and pollution control (IWHT2021). Heat Transfer Engineering, 45(7-8), 585-586. https://doi.org/10.1080/01457632.2023.2213990
  33. Xiao, S., Li, L., Ma, J., Liu, D., & Li, J. (2023). A study of residents’ intentions to participate in the renovation of older communities under the perspective of urban renewal: evidence from Zhangjiakou, China. Journal of Asian Architecture and Building Engineering, 22(3), 1094-1109. https://doi.org/10.1080/13467581.2023.2182643
  34. Xu, Y., & Juan, Y. K. (2022). Optimal decision-making model for outdoor environment renovation of old residential communities based on WELL Community Standards in China. Architectural Engineering and Design Management, 18(5), 571-592. https://doi.org/10.1080/17452007.2021.1926900
  35. Yan, Z., Zhu, X., Wang, X., Ye, Z., Guo, F., Xie, L., & Zhang, G. (2023). A multi-energy load prediction of a building using the multi-layer perceptron neural network method with different optimization algorithms. Energy Exploration and Exploitation, 41(1), 273-305. https://doi.org/10.1177/01445987221112250
  36. Yildiz, B. S., Pholdee, N., Bureerat, S., Yildiz, A. R., & Sait, S. M. (2022). Enhanced grasshopper optimization algorithm using elite opposition-based learning for solving real-world engineering problems. Engineering with Computers, 38(5), 4207-4219. https://doi.org/10.1007/s00366-021-01368-w
  37. Zhang, J., Sethi, S. P., Choi, T. M., & Cheng, T. C. E. (2022). Pareto optimality and contract dependence in supply chain coordination with risk‐averse agents. Production and Operations Management, 31(6), 2557-2570. https://doi.org/10.1111/poms.13701
  38. Zhang, Y., Tian, Y., & Zhang, X. (2023). Improved SparseEA for sparse large-scale multi-objective optimization problems. Complex and Intelligent Systems, 9(2), 1127-1142. https://doi.org/10.1007/s40747-021-00553-0
  39. Zheng, S., Lyu, Z., & Foong, L. K. (2022). Early prediction of cooling load in energy-efficient buildings through novel optimizer of shuffled complex evolution. Engineering with Computers, 38(Suppl 1), 105-119. https://doi.org/10.1007/s00366-020-01140-6
  40. Zheng, Z., Zhang, K. T., & Gao, X. Q. (2022). Human-cyber-physical system for production and operation decision optimization in smart steel plants. Science China Technological Sciences, 65(2), 247-260. https://doi.org/10.1007/s11431-020-1838-6

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