Comprehensive evaluation of multi-energy complementary ecosystem based on improved multicriteria decision-making method

The multi-energy complementary ecosystem is an important form of the modern energy system.However,standardized evaluation criteria and the corresponding method framework have not yet been formed,resulting in unclear standards and irregular processes of its construction.To cope with this issue,a novel comprehensive evaluation framework for multi-energy complementary ecosystems is proposed in this study.First,a 5D comprehensive evaluation criteria system,including environment,economy,technology,safety and systematicness,is constructed.Then,a novel multicriteria decision-making model integrating an analytic network process,entropy and preference-ranking organization method for enrichment evaluation under an intuitional fuzzy environment is proposed.Finally,four practical cases are used for model testing and empirical analysis.The results of the research show that the unit cost of the energy supply and the internal rate of return indexes have the highest weights of 0.142 and 0.010,respectively.It means that they are the focus in the construction of a multi-energy complementary ecosystem.The net flows of four cases are 0.015,0.123,-0.132 and-0.005,indicating that cases with a variety of energy supply forms and using intelligent management and control platforms to achieve cold,heat and electrical coupling have more advantages.

A novel collaborative decision-making method based on generalized abductive learning for resolving design conflicts

In complex product design,lots of time and resources are consumed to choose a preference-based compromise decision from non-inferior preliminary design models with multi-objective conflicts.However,since complex products involve intensive multi-domain knowledge,preference is not only a comprehensive representation of objective data and subjective knowledge but also characterized by fuzzy and uncertain.In recent years,enormous challenges are involved in the design process,within the increasing complexity of preference.This article mainly proposes a novel decision-making method based on generalized abductive learning(G-ABL)to achieve autonomous and efficient decision-making driven by data and knowledge collaboratively.The proposed G-ABL framework,containing three cores:classifier,abductive kernel,and abductive machine,supports preference integration from data and fuzzy knowledge.In particular,a subtle improvement is presented for WK-means based on the entropy weight method(EWM)to address the local static weight problem caused by the fixed data preferences as the decision set is locally invariant.Furthermore,fuzzy comprehensive evaluation(FCE)and Pearson correlation are adopted to quantify domain knowledge and obtain abducted labels.Multi-objective weighted calculations are utilized only to label and compare solutions in the final decision set.Finally,an engineering application is provided to verify the effectiveness of the proposed method,and the superiority of which is illustrated by comparative analysis.