データセンター向け低炭素冷却・エネルギー管理技術評価のためのT球状ファジィ値ニュートロソフィックMEREC-EDASフレームワーク
T-Spherical Fuzzy-Valued Neutrosophic MEREC-EDAS Framework for Evaluating Low-Carbon Cooling and Energy Management Technologies for Data Centers (原題)
Nhat‐Luong Nhieu, Hoang‐Kha Nguyen
🤖 gxceed AI 要約
日本語
本研究は、データセンターの低炭素冷却・エネルギー管理技術を評価するためのT-SFVNSベースのMEREC-EDASフレームワークを開発。30名の専門家評価に基づき、9技術を10基準で評価し、炭素削減ポテンシャル、電力需要削減、運用コスト効率などを重視。直接チップ液冷と液浸冷却が最有力と判明。
English
This study develops a T-Spherical Fuzzy-Valued Neutrosophic MEREC-EDAS framework to evaluate low-carbon cooling and energy management technologies for data centers. Based on assessments from 30 experts, nine technologies are ranked against ten criteria, with carbon reduction potential and electricity demand reduction as top weights. Direct-to-chip liquid cooling and liquid immersion cooling emerge as leading options.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本のデータセンターは電力消費増大が課題であり、省エネ法やGXリーグの枠組みで低炭素技術の導入が求められる。本フレームワークは、不確実性下での技術選定を支援し、投資判断や報告書での根拠提供に寄与する。
In the global GX context
Globally, data centers face pressure to decarbonize under frameworks like the EU Energy Efficiency Directive and SEC climate disclosure. This MCDM framework offers a transparent, reproducible method for technology selection, supporting corporate sustainability reporting and transition finance decisions.
👥 読者別の含意
🔬研究者:Provides a novel fuzzy MCDM application for technology evaluation, with robustness checks that inform decision-analysis methodology.
🏢実務担当者:Offers a structured approach for selecting low-carbon cooling technologies, aiding in capex planning and sustainability reporting.
🏛政策担当者:Highlights criteria like carbon reduction and energy efficiency that could inform standards or incentives for data center decarbonization.
📄 Abstract（原文）
Fuzzy multi-criteria decision-making is important for technology assessment when expert judgments contain uncertainty, hesitation, and inconsistent evidence. This study develops a T-Spherical Fuzzy-Valued Neutrosophic Set (T-SFVNS)-based MEREC-EDAS framework for evaluating low-carbon cooling and energy-management technologies for data centers. Expert linguistic assessments are represented by T-Spherical Fuzzy-Valued Neutrosophic Numbers and aggregated before a score function is used at the explicit scalarization boundary. Standard MEREC then derives objective criterion weights from criterion-removal effects, and standard EDAS ranks alternatives by their positive and negative distances from the average score profile. The application evaluates nine technologies against ten criteria using assessments from thirty domain specialists. The corrected MEREC calculation assigns the greatest weights to carbon reduction potential (0.127), electricity demand reduction (0.125), maintenance complexity (0.124), operational cost efficiency (0.123), and cooling efficiency (0.123). The final ranking is Direct-to-Chip Liquid Cooling, Liquid Immersion Cooling, AI-Enabled Energy Management, Water-Side Free Cooling, Free-Air Cooling, Rear-Door Heat Exchanger Cooling, Hot/Cold Aisle Containment, Renewable-Powered Cooling, and Thermal Storage-Assisted Cooling. Weight perturbation, q-parameter, leave-one-expert-out, alternative-deletion, dominated-alternative, and multi-method comparisons show that the leading tier is robust, although the exact order of the two liquid-cooling technologies is sensitive in some scenarios. The findings provide a transparent and reproducible decision-support basis while explicitly acknowledging the information compression and rank-reversal limitations of score-based MCDM.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/systems14091039first seen 2026-08-26 04:39:58
🔔 こうした論文の新着を逃したくない方は キーワードアラート に登録（無料・3キーワードまで）。
gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。