Multi-criteria group decision-making framework for offshore wind farm site selection based on the intuitionistic linguistic aggregation operators

Gao, Jianwei; Guo, Fengjia; Ma, Zhen; Huang, Xin; Li, Xiangzhen

doi:10.1016/j.energy.2020.117899

Cited by 58 publications

(20 citation statements)

References 43 publications

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“…The majority of MpMcDM models under a fuzzy context in the literature consider two main approaches: (i) expert aggregation followed by criteria aggregation [113], [114], or (ii) criteria aggregation followed by expert aggregation [115]. The first of these two approaches is the most extended in MpMcDM, and its workflow (see Figure 3) is divided into two phases [17]: .…”

Section: Multi-person Multi-criteria Decision-making (Mpmcdm)mentioning

confidence: 99%

Revisiting Fuzzy and Linguistic Decision Making: Scenarios and Challenges for Making Wiser Decisions in a Better Way

Liu

Palomares

et al. 2021

IEEE Trans. Syst. Man Cybern, Syst.

157

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This paper provides a brief tour through the main fuzzy and linguistic decision-making trends, studies, methodologies and models developed in the last 50 years. Fuzzy and linguistic decision-making approaches allow to address complex real-world decision problems where humans exhibit vagueness, imprecision and/or use natural language to assess decision alternatives, criteria, etc. The aim of this paper is threefold. Firstly, the main fuzzy set theory and computing with words based representation paradigms of decision information, with their different levels of expressive richness and complexity, are reviewed. Secondly, three core decision-making frameworks are examined:(1) multi-criteria decision-making, (2) group consensus-driven decision-making, and (3) multi-person multi-criteria decisionmaking. Thirdly, the paper discusses new complex decisionmaking frameworks that have emerged in recent years, where decisions are guided by the "wisdom of the crowd": their associated challenges are highlighted and considerations on much needed key guidelines for future research in the field are provided.

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Section: Multi-person Multi-criteria Decision-making (Mpmcdm)mentioning

confidence: 99%

Revisiting Fuzzy and Linguistic Decision Making: Scenarios and Challenges for Making Wiser Decisions in a Better Way

Liu

Palomares

et al. 2021

IEEE Trans. Syst. Man Cybern, Syst.

157

View full text Add to dashboard Cite

show abstract

“…The next step was to define the evaluation criteria used in the decision problem. Literature review [14,[18][19][20][21][22], analysis of available information on planned investments [31][32][33][34][35][36][37] and consultation with field experts made it possible to indicate the criteria presented in Table 1. Experts -individually determined the weights of the criteria presented in Table 2.…”

Section: Model Of the Decision Problemmentioning

confidence: 99%

“…In turn, Wu et al, using the intuitionistic fuzzy ELECTRE III method assessed the locations for the offshore wind farms in Shandong in China [20]. Gao et al examined a similar decision problem in this region using the set of intuitionistic linguistic ordered weighted averaging operators [21]. Deveci et al used the interval-valued fuzzy rough based Delphi method to assess criteria used in decision problems related to the location of offshore wind farms [14].…”

Section: Introductionmentioning

confidence: 99%

Multi-Criteria Fuzzy Evaluation of the Planned Offshore Wind Farm Investments in Poland

Ziemba

2021

Energies

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In recent years, the dynamic development of renewable energy has been visible all over the world, including Poland. Wind energy is one of the most used renewable energy sources. In Poland, by 2030, it is planned to commission at least six offshore wind farms with a total capacity of 3.8 GW. It is estimated that these investments will increase Poland’s GDP by approximately PLN 60 billion and increase tax revenues by PLN 15 billion. Therefore, they could be a strong stimulus for the development of the Polish economy and may be of great importance in recovering from the crisis caused by the economic constraints related to the COVID-19 pandemic. The aim of the article is a multi-criteria evaluation of the investments planned in Poland in offshore wind farms and identification of potentially the most economically effective investments. To account for the uncertainty in this decision problem, a modified fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method was used and a comprehensive sensitivity analysis was performed. As a result of the research, a ranking of the considered projects was constructed and the most preferred investments were identified. Moreover, it has been shown that all the investments considered are justified and recommended.

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“…This article found through literature search that the factors affecting global offshore wind power investment include installed capacity, average annual power generation, number of units, engineering level, commissioning time, area, single unit capacity, sweeping area, and unit Weight, steel price, wind wheel diameter, submarine cable, hub height, number of blades, water depth, distance from shore, rated wind speed, limit wind speed, etc. [44,45]. In order to accurately determine the level of investment, it is necessary to accurately select the influencing factors.…”

Section: Independent Variablesmentioning

confidence: 99%

Forecast Research on Multidimensional Influencing Factors of Global Offshore Wind Power Investment Based on Random Forest and Elastic Net

Niu

et al. 2021

Sustainability

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Recently, countries around the world have begun to develop low-carbon energy sources to alleviate energy shortage and cope with climate change. The offshore wind power has become a new direction for clean energy exploration. However, the accuracy of offshore wind power investment is still an urgent problem due to its complexity. Therefore, this paper investigates offshore wind power investment to improve the investment forecasting accuracy. In this study, the random forest (RF) algorithm was used to screen out the key factors influencing multi-dimensional global offshore wind power investment, and the elastic net (EN) was optimized using the ADMM algorithm and used in the global offshore wind power investment forecast model. The results show that the adoption of the random forest algorithm can effectively screen out the key influencing factors of global offshore wind power investment. Water depth, offshore distance and sweeping area have the most influence on the investment. Moreover, compared with other models, the elastic net optimized by ADMM can better reflect the changing trend of global offshore wind power investment, with smaller errors and a higher regression accuracy. The application of the RF–EN combined model can screen out effective factors from complex multi-dimensional influencing factors, and perform high-precision regression analysis, which is conducive to improving the global offshore wind power investment forecast. The conclusion obtained can set a more reasonable plan for the future construction and investment of global offshore wind power projects.

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Multi-criteria group decision-making framework for offshore wind farm site selection based on the intuitionistic linguistic aggregation operators

Cited by 58 publications

References 43 publications

Revisiting Fuzzy and Linguistic Decision Making: Scenarios and Challenges for Making Wiser Decisions in a Better Way

Revisiting Fuzzy and Linguistic Decision Making: Scenarios and Challenges for Making Wiser Decisions in a Better Way

Multi-Criteria Fuzzy Evaluation of the Planned Offshore Wind Farm Investments in Poland

Forecast Research on Multidimensional Influencing Factors of Global Offshore Wind Power Investment Based on Random Forest and Elastic Net

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