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Integrated hesitant fuzzy-based decision-making framework for evaluating sustainable and renewable energy

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Abstract

Energy can be generated from renewable sources such as geothermal heat, rain, wind, tides, and sunlight. All nations' economies and political systems depend on their access to energy resources. That is, why it is so important for any nation to make the most prudent energy investment decisions possible. Because of climate change, every country has announced its intention to include more renewable energy sources in their energy portfolios. In this work, the authors present hesitant fuzzy multi-factor decision analysis tactics for choosing the best renewable energy sources, building on the insights of a hesitant fuzzy set system, a valuable tool for handling indecision in the occurrence of ambiguous or incomplete data. Based on a hesitant fuzzy analytical hierarchy process technique, this study ranks preferences according to how closely they match an ideal outcome. This is in line with specialized valuation scores that can be written as semantic expressions, hesitant fuzzy numbers, or crisp numbers. Hesitant fuzzy set concepts form the basis of this combined method, and they are used to objectively or subjectively weigh alternatives in light of specific domain-specific requirements. With the proposed strategy, the best renewable energy choice will be identified. According to the achieved results, landfill gas and biogas have the highest rank among the alternatives to renewable energy. The achieved results are compared with another method of fuzzy multicriteria decision-making, and it shows that the hesitant fuzzy decision-making method is found to be the most accurate in providing results on the selection of renewable energy resources. Furthermore, it will help government officials and related individuals decide on better sustainable and renewable energy resources that can cost less and give more sustainable results in the future.

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The data used in this study are available upon request from the corresponding author.

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The authors have not received any specific funding for this study. This pursuit is a part of their scholarly endeavors.

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Authors and Affiliations

  1. Department of Computer Science and Information Systems, Shri Ramswaroop Memorial University, Lucknow-Deva Road, Barabanki, Uttar Pradesh, 225003, India

    Kavita Sahu & Bineet Kumar Gupta

  2. Department of Computer Science, Dr. Shakuntala Misra National Rehabilitation University, Lucknow, Uttar Pradesh, 226017, India

    R. K. Srivastava

  3. Department of Computer Science and Engineering, Babu Banarasi Das University, Lucknow, Uttar Pradesh, India

    Sarvesh Kumar & Ravi Prakash Verma

  4. Feroze Gandhi Institute of Professional Studies, Raebareli, Uttar Pradesh, India

    Manish Saxena

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  1. Kavita Sahu
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Sahu, K., Srivastava, R.K., Kumar, S. et al. Integrated hesitant fuzzy-based decision-making framework for evaluating sustainable and renewable energy. Int J Data Sci Anal 16, 371–390 (2023). https://doi.org/10.1007/s41060-023-00426-4

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