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A novel consensus model with probabilistic linguistic preference relation for the utilization mode selection of renewable energy sources

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Abstract

The exploitation and utilization of renewable energy is an important issue that contributes to the sustainable development of the national economy. This paper deals with the utilization mode selection of renewable energy in a group decision making (GDM) problem with multiple stakeholders whose assessments are represented as the probabilistic linguistic preference relations (PLPRs). For GDM problems with PLPRs, two requirements should be satisfied before selection process: individual consistency and group consensus. In this regard, a premetric-based consensus measure of PLPRs is defined to describe the consensus level among stakeholders and a consensus improvement model is developed to modify the identified PLPRs while guaranteeing the consistency of the adjusted PLPRs. Then, the proposed method is applied to solve the utilization modes selection of multiple renewable energy in the Jinsha River upper reaches. Finally, the robustness and comparative analysis validate the good adaptability, high efficiency and better practical use values of this method.

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References

  1. Bilal BO, Sambou V, Ndiaye PA et al (2010) Optimal design of a hybrid solar–wind-battery system using the minimization of the annualized cost system and the minimization of the loss of power supply probability (LPSP). Renew Energy 35(10):2388–2390

    Article  Google Scholar 

  2. Chodkowska-Miszczuk J, Martinat S, Cowell R (2019) Community tensions, participation, and local development: Factors affecting the spatial embeddedness of anaerobic digestion in Poland and the Czech Republic. Energy Res Soc Sci 55:134–145

    Article  Google Scholar 

  3. Feng X, Pang X, Zhang L (2020) On consistency and priority weights for interval probabilistic linguistic preference relations. Fuzzy Optimization Decis Mak 19(4):529–560

    Article  MathSciNet  MATH  Google Scholar 

  4. Freeman RE (2010) Strategic Management: A Stakeholder Approach. Cambridge University Press, Cambridge

    Book  Google Scholar 

  5. Gao J, Xu Z, Liang Z et al (2019) Expected consistency-based emergency decision making with incomplete probabilistic linguistic preference relations. Knowl Based Syst 176:15–28

    Article  Google Scholar 

  6. Gao J, Xu Z, Ren P et al (2019) An emergency decision making method based on the multiplicative consistency of probabilistic linguistic preference relations. Int J Mach Learn Cybern 10(7):1613–1629

    Article  Google Scholar 

  7. Herrera F, Herrera-Viedma E, Verdegay JL (1996) A model of consensus in group decision making under linguistic assessments. Fuzzy Sets Syst 79(1):73–87

    Article  MathSciNet  MATH  Google Scholar 

  8. Hou F (2015) The prametric-based GDM selection procedure under linguistic assessments. In: IEEE International Conference on Fuzzy Systems, pp 1–8.

  9. Hou F (2019) Triantaphyllou E. An iterative approach for achieving consensus when ranking a finite set of alternatives by a group of experts. Eur. J. Oper. Res 275(2):570–579

    Article  MATH  Google Scholar 

  10. Hu G (2019) Exploration on utilization models for renewable energy of sichuan-tibet section in the jinsha river upper reaches. Huadian Technol 11(41):62–65

    Google Scholar 

  11. Liu A, Qiu H, Lu H et al (2019) A consensus model of probabilistic linguistic preference relations in group decision making based on feedback mechanism. IEEE Access 7:148231–148244

    Article  Google Scholar 

  12. Liu PD, Li, Y, Zhang XH, Pedrycz W (2022) A Multiattribute group decision-making method with probabilistic linguistic information based on an adaptive consensus reaching model and evidential reasoning. IEEE Trans. Cybern. https://doi.org/10.1109/TCYB.2022.3165030

  13. Liu PD, Zhang K, Wang P, Wang F (2022) A clustering-and maximum consensus-based model for social network large-scale group decision making with linguistic distribution. Inf Sci 602:269–297

    Article  Google Scholar 

  14. Mercer N, Sabau G, Klinke A (2017) “Wind energy is not an issue for government”: Barriers to wind energy development in Newfoundland and Labrador, Canada. Energy Policy 108:673–683

    Article  Google Scholar 

  15. Pang Q, Wang H, Xu Z (2016) Probabilistic linguistic term sets in multi-attribute group decision making. Inf Sci 369:128–143

    Article  Google Scholar 

  16. Richards G, Noble B, Belcher K (2012) Barriers to renewable energy development: a case study of large-scale wind energy in Saskatchewan, Canada. Energy Policy 42:691–698

    Article  Google Scholar 

  17. Song Y, Hu J (2019) Large-scale group decision making with multiple stakeholders based on probabilistic linguistic preference relation. Appl Soft Comput 80:712–722

    Article  Google Scholar 

  18. Wang L, Singh C (2009) Multicriteria design of hybrid power generation systems based on a modified particle swarm optimization algorithm. IEEE Trans Energy Convers 24(1):163–172

    Article  Google Scholar 

  19. Xu ZS (2004) EOWA and EOWG operators for aggregating linguistic labels based on linguistic preference relations. Int. J. Uncertain. Fuzziness Knowl. Based Syst 12(6): 791–810.

  20. Xu ZS (2006) Incomplete linguistic preference relations and their fusion. Inf Fusion 7(3):331–337

    Article  Google Scholar 

  21. Zhang YX, Xu ZS, Liao HC (2017) A consensus process for group decision making with probabilistic linguistic preference relations. Inf Sci 414:260–275

    Article  MATH  Google Scholar 

  22. Zhang YX, Xu ZS, Wang H et al (2016) Consistency-based risk assessment with probabilistic linguistic preference relation. Appl Soft Comput 49:817–833

    Article  Google Scholar 

  23. Zhang YX, Xu ZS, Liao HC (2018) An ordinal consistency-based group decision making process with probabilistic linguistic preference relation. Inf Sci 467:179–198

    Article  MathSciNet  MATH  Google Scholar 

  24. Zhou H, Zhu N, Yang C et al (2018) Comprehensive performance evaluation method of hybrid renewable energy system based on exergy theory. Build Energy 46(8):53–58

    Google Scholar 

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Correspondence to Fujun Hou.

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You, X., Hou, F. A novel consensus model with probabilistic linguistic preference relation for the utilization mode selection of renewable energy sources. Int. J. Mach. Learn. & Cyber. 14, 1845–1861 (2023). https://doi.org/10.1007/s13042-022-01733-1

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  • DOI: https://doi.org/10.1007/s13042-022-01733-1

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