[论文解读] Subjective-Objective Median-based Importance Technique (SOMIT) to Aid Multi-Criteria Renewable Energy Evaluation

Ding Ding, Yang Li+3 more|arXiv (Cornell University)|Jan 6, 2026

Multi-Criteria Decision Making被引用 0

一句话总结

SOMIT 是一种用于可再生能源多属性决策的混合主观-客观加权方法，减少比较次数、提升鲁棒性，并实现与 TOPSIS 的模块化整合。

ABSTRACT

Accelerating the renewable energy transition requires informed decision-making that accounts for the diverse financial, technical, environmental, and social trade-offs across different renewable energy technologies. A critical step in this multi-criteria decision-making (MCDM) process is the determination of appropriate criteria weights. However, deriving these weights often solely involves either subjective assessment from decision-makers or objective weighting methods, each of which has limitations in terms of cognitive burden, potential bias, and insufficient contextual relevance. This study proposes the subjective-objective median-based importance technique (SOMIT), a novel hybrid approach for determining criteria weights in MCDM. By tailoring SOMIT to renewable energy evaluation, the method directly supports applied energy system planning, policy analysis, and technology prioritization under carbon neutrality goals. The practical utility of SOMIT is demonstrated through two MCDM case studies on renewable energy decision-making in India and Saudi Arabia. Using the derived weights from SOMIT, the TOPSIS method ranks the renewable energy alternatives, with solar power achieving the highest performance scores in both cases. The main contributions of this work are five-fold: 1) the proposed SOMIT reduces the number of required subjective comparisons from the conventional quadratic order to a linear order; 2) SOMIT is more robust to outliers in the alternatives-criteria matrix (ACM); 3) SOMIT balances subjective expert knowledge with objective data-driven insights, thereby mitigating bias; 4) SOMIT is inherently modular, allowing both its individual parts and the complete approach to be seamlessly coupled with a wide range of MCDM methods commonly applied in energy systems and policy analysis; 5) a dedicated Python library, pysomit, is developed for SOMIT.

研究动机与目标

• Reduce the cognitive burden of weight elicitation in MCDM for renewable energy decisions.
• Balance expert subjective judgments with objective data insights to mitigate bias.
• Provide a modular weighting approach that can be integrated with various MCDM methods.
• Demonstrate the utility of SOMIT in real-world energy planning under carbon neutrality goals.

提出的方法

• Introduce SOMIT as a hybrid subjective-objective weighting technique for MCDM in renewable energy evaluation.
• Show that SOMIT reduces subjective comparisons from quadratic to linear order.
• Demonstrate SOMIT’s robustness to outliers in the alternatives-criteria matrix.
• Couple SOMIT with the TOPSIS method to rank renewable energy alternatives.
• Develop a Python library, pysomit, for implementing SOMIT.

实验结果

研究问题

• RQ1 How can criteria weights be determined in MCDM for renewable energy in a way that reduces cognitive burden while incorporating both subjective and objective inputs?
• RQ2 Does SOMIT improve robustness to outliers compared with traditional weighting methods?
• RQ3 Can SOMIT be integrated with common MCDM methods (e.g., TOPSIS) to produce actionable rankings for energy technologies?
• RQ4 Is SOMIT modular enough to be coupled with various algorithms and data sources in energy policy analysis?

主要发现

• SOMIT reduces the number of required subjective comparisons from quadratic to linear order.
• SOMIT is more robust to outliers in the alternatives-criteria matrix.
• SOMIT balances subjective expert knowledge with objective data-driven insights, reducing bias.
• SOMIT is modular and can be coupled with a wide range of MCDM methods.
• A dedicated Python library, pysomit, supports SOMIT implementation.