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<p>Renewable energy will reduce the strain on the energy supply to some degree; however, many challenges exist in its organic integration with the current energy system, thus prompting a new round of transformation of the existing energy system. Inspired by the Internet concepts, methods, and technologies, the Energy Internet, an open and equal facility for convenient access and intelligent use of energy throughout the chain from production and transmission to consumption, has become a significant development trend. Energy, big data has enormous potential value in facilitating the demand-driven allocation of energy resources and the optimization and transition of the energy structure. The green quality evaluation of metropolis energy big data belongs to the MAGDM category. Recently, ExpTODIM and PROMETHEE techniques have been applied to solve MAGDM problems. In the green quality evaluation of metropolis’ energy big data, probabilistic linguistic term sets (PLTSs) characterize uncertain information. In this paper, the probabilistic linguistic ExpTODIM-MABAC (PL-ExpTODIM-MABAC) technique is constructed and proposed to solve MAGDM problems with PLTSs. The MEREC technique obtains weights under PLTSs. Finally, an example of the green quality evaluation of metropolis’ energy big data is provided to demonstrate the ExpTODIM-MABAC approach.</p>

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A system-based evaluation and feedback mechanism for green quality of metropolis energy big data using probabilistic linguistic term sets

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月4日

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  • Ren Wang,
  • Dan Peng

摘要

Renewable energy will reduce the strain on the energy supply to some degree; however, many challenges exist in its organic integration with the current energy system, thus prompting a new round of transformation of the existing energy system. Inspired by the Internet concepts, methods, and technologies, the Energy Internet, an open and equal facility for convenient access and intelligent use of energy throughout the chain from production and transmission to consumption, has become a significant development trend. Energy, big data has enormous potential value in facilitating the demand-driven allocation of energy resources and the optimization and transition of the energy structure. The green quality evaluation of metropolis energy big data belongs to the MAGDM category. Recently, ExpTODIM and PROMETHEE techniques have been applied to solve MAGDM problems. In the green quality evaluation of metropolis’ energy big data, probabilistic linguistic term sets (PLTSs) characterize uncertain information. In this paper, the probabilistic linguistic ExpTODIM-MABAC (PL-ExpTODIM-MABAC) technique is constructed and proposed to solve MAGDM problems with PLTSs. The MEREC technique obtains weights under PLTSs. Finally, an example of the green quality evaluation of metropolis’ energy big data is provided to demonstrate the ExpTODIM-MABAC approach.