TY - JOUR
T1 - Sustainability prioritization of energy systems under hybrid information and missing information based on the improved grey relational analysis
AU - Lin, Ruojue
AU - Ren, Jingzheng
AU - Liu, Yue
AU - Lee, Carman K.M.
AU - Ji, Ping
AU - Zhang, Long
AU - Man, Yi
N1 - Funding Information:
The work described in this paper was supported by the grant from the Research Committee of The Hong Kong Polytechnic University under student account code RK22 and was also financially supported by the Hong Kong Research Grants Council for Early Career Scheme (Grand No. 25208118 ) and The Postdoctoral Fellowships Scheme (G-YW4Y).
Publisher Copyright:
© 2021 Elsevier Ltd
PY - 2021/10
Y1 - 2021/10
N2 - Life cycle sustainability of energy systems has received more and more attentions recently. In order to make an accurate comparison of the sustainability performance of different energy systems and promote the decision-making process, various prioritization methods of energy systems were developed. However, the lack of enough data for decision-making usually limits the accuracy of the prioritization. On the one hand, the decision-makers can only collect multiple types of data resources with hybrid information, for example, data in the formats of crisp numbers, interval numbers, and fuzzy numbers. On the other hand, some information for certain alternatives with respect to certain criteria is hard to be obtained. Therefore, it is of vital importance to achieve sustainability-oriented prioritization of energy systems under hybrid information and missing information. This study aims at developing a prioritization framework for energy systems ranking with missing information and hybrid information. An improved Grey Relational Analysis (GRA) is extended from the classical GRA method to handle hybrid information in this study. An innovative method to quantify linguistic expressions is proposed to deal with missing information. A hypothetical case study regarding electricity generation scenarios selection was used to evaluate the feasibility of this proposed framework. Sensitivity analysis was also conducted, and the results showed that the iGRA-MH is feasible in handling hybrid and missing information and it performs more stable than other multi-criteria decision-making models.
AB - Life cycle sustainability of energy systems has received more and more attentions recently. In order to make an accurate comparison of the sustainability performance of different energy systems and promote the decision-making process, various prioritization methods of energy systems were developed. However, the lack of enough data for decision-making usually limits the accuracy of the prioritization. On the one hand, the decision-makers can only collect multiple types of data resources with hybrid information, for example, data in the formats of crisp numbers, interval numbers, and fuzzy numbers. On the other hand, some information for certain alternatives with respect to certain criteria is hard to be obtained. Therefore, it is of vital importance to achieve sustainability-oriented prioritization of energy systems under hybrid information and missing information. This study aims at developing a prioritization framework for energy systems ranking with missing information and hybrid information. An improved Grey Relational Analysis (GRA) is extended from the classical GRA method to handle hybrid information in this study. An innovative method to quantify linguistic expressions is proposed to deal with missing information. A hypothetical case study regarding electricity generation scenarios selection was used to evaluate the feasibility of this proposed framework. Sensitivity analysis was also conducted, and the results showed that the iGRA-MH is feasible in handling hybrid and missing information and it performs more stable than other multi-criteria decision-making models.
KW - Grey relational analysis
KW - Hybrid information
KW - Life cycle sustainability
KW - Multi-criteria decision making
KW - Sustainability
UR - http://www.scopus.com/inward/record.url?scp=85113776489&partnerID=8YFLogxK
U2 - 10.1016/j.seta.2021.101543
DO - 10.1016/j.seta.2021.101543
M3 - Journal article
AN - SCOPUS:85113776489
SN - 2213-1388
VL - 47
JO - Sustainable Energy Technologies and Assessments
JF - Sustainable Energy Technologies and Assessments
M1 - 101543
ER -