An endo-confidence-based consensus with hierarchical clustering and automatic feedback in multi-attribute large-scale group decision-making

Tian, Xiaoli; Li, Wanqing; Xu, Zeshui; Kou, Gang; Nie, Chuming

doi:10.1016/j.ins.2022.07.042

Cited by 20 publications

(1 citation statement)

References 48 publications

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“…For large-scale photovoltaic data, the clustering accuracy and efficiency cannot be satisfied at the same time. Multi clustering is used to process the original data hierarchically and divide the clusters (Xiao-li et al, 2022). For the problems to be solved for data, specific clustering methods are selected according to the characteristics of each layer of data set, which can improve the clustering accuracy and ensure the clustering efficiency.…”

Section: Multiple Clusteringmentioning

confidence: 99%

Short-term prediction of PV output based on weather classification and SSA-ELM

Cai²,

Yang³

et al. 2023

Front. Energy Res.

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In this paper, according to the power output characteristics of distributed photovoltaic users, the SSA-ELM (Sparrow Search Algorithm - Extreme Learning Machine) model based on weather type division is proposed for photovoltaic power day ahead prediction. Because the solar panel power generation sequence of photovoltaic users contains high frequency fluctuations, in this paper we use the power sequence convergence effect to make cluster prediction on all photovoltaic panels to reduce the randomness of distributed photovoltaic. The prediction accuracy is further improved by dividing weather types. The historical data of distributed PV users in a region of Gansu province is used for modeling verification, and the results show that the prediction error of the proposed method is lower. In bad weather, the root mean square error is at least 0.02 less than the comparison model, and the average annual accuracy rate is 93.2%, which proves the applicability of the proposed method in different output types.

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Section: Multiple Clusteringmentioning

confidence: 99%

Short-term prediction of PV output based on weather classification and SSA-ELM

Cai²,

Yang³

et al. 2023

Front. Energy Res.

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Online reviews-oriented hotel selection: A large-scale group decision-making method based on the expectations of decision makers

2022

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A large-scale multi-attribute group decision-making method with R-numbers and its application to hydrogen fuel cell logistics path selection

Cheng,

Fan,

et al. 2024

Complex Intell. Syst.

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The large-scale multi-attribute group decision-making (LSMAGDM) problem has become a hot research topic in the field of decision science. An R-numbers large-scale multi-attribute group decision-making (R-LSMAGDM) model is proposed to be constructed in this paper based on the advantages of R-numbers in capturing risks. First, the most commonly used clustering method, k-means, is introduced to determine the sub-groups. Then, a new sub-group weighting determination model is constructed by considering sub-group size and sub-group entropy. Next, we also build an optimized consensus-reaching model by improving the calculation method of the mean value. Then, the R-numbers weighted Hamy mean (RNWHM) operator is proposed to aggregate the sub-group information. In addition, the logarithmic percentage change-driven objective weighting (LOPCOW) method and the compromise ranking of alternatives from distance to ideal solution (CRADIS) method are used for attribute weighting calculation and alternative ranking, respectively. Finally, the effectiveness of the model is verified by an application example of hydrogen fuel cell logistics path selection.

show abstract

An endo-confidence-based consensus with hierarchical clustering and automatic feedback in multi-attribute large-scale group decision-making

Cited by 20 publications

References 48 publications

Short-term prediction of PV output based on weather classification and SSA-ELM

Short-term prediction of PV output based on weather classification and SSA-ELM

Online reviews-oriented hotel selection: A large-scale group decision-making method based on the expectations of decision makers

A large-scale multi-attribute group decision-making method with R-numbers and its application to hydrogen fuel cell logistics path selection

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