Abstract
The efficiency evaluation model of integrated energy system, involving many influencing factors, and the attribute values are heterogeneous and non-deterministic, usually cannot give specific numerical or accurate probability distribution characteristics, making the final evaluation result deviation. According to the characteristics of the integrated energy system, a hybrid multi-attribute decision-making model is constructed. The evaluation model considers the decision maker’s risk preference. In the evaluation of the efficiency of the integrated energy system, the evaluation value of some evaluation indexes is linguistic value, or the evaluation value of the evaluation experts is not consistent. These reasons lead to ambiguity in the decision information, usually in the form of uncertain linguistic values and numerical interval values. In this paper, the risk preference of decision maker is considered when constructing the evaluation model. Interval-valued multiple-attribute decision-making method and fuzzy linguistic multiple-attribute decision-making model are proposed. Finally, the mathematical model of efficiency evaluation of integrated energy system is constructed.
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Acknowledgements
(1) Project supported by the humanities and social sciences research youth fund of the China Ministry education, the project number: 16YJC790052
(2) Hunan province “The 13th Five-year” education planning project” School-enterprise cooperation in personnel training Virtual Alliance: organizational structure, resource sharing and performance assessment study” (project number: XJK015BGD053).
(3) 2015 Hunan Province Philosophy and Social Science Fund Project “ Research on the Internet Media Effect China’s Stock Market and Network Public Opinion Monitor Index of Listed Companies” (project number: 14YBA306).
(4) Supported by the constructing program of the key discipline of Finance in Huai-hua University.
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Li, Y. Research on efficiency evaluation model of integrated energy system based on hybrid multi-attribute decision-making. Environ Sci Pollut Res 26, 17866–17874 (2019). https://doi.org/10.1007/s11356-017-9100-8
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DOI: https://doi.org/10.1007/s11356-017-9100-8