Powering the Green Transition in Quad-Sectors with Hybrid Clean Energy Technologies
ハイブリッドクリーンエネルギー技術によるクワッドセクターのグリーン移行の推進 (AI 翻訳)
Helena M. Ramos, Chetan Rishi, Óscar E. Coronado-Hernández, Modesto Pérez‐Sánchez, Paul Coughlan, Aonghus McNabola
🤖 gxceed AI 要約
日本語
本研究は、太陽光・風力・水力・蓄電を統合したハイブリッド再生可能エネルギーシステム（HRES）の導入候補地を評価するため、機械学習（EL-SVM、決定木、ロジスティック回帰）とAHPを組み合わせたMCDAフレームワークを提案する。HY4RESプロジェクトの4つのパイロットサイト（農村、養殖、港湾、農業）を対象に、技術・環境・社会・経済のKPIを評価し、農村サイトが最適と判定された。MLはAHPの一貫性チェックと感度分析を効率化し、意思決定を支援する。
English
This study proposes an MCDA framework combining machine learning (EL-SVM, decision tree, logistic regression) with AHP to evaluate candidate sites for hybrid renewable energy systems (HRES) integrating solar, wind, hydropower, and storage. Four pilot sites (rural, aquaculture, port, agriculture) from the HY4RES project are assessed using technical, environmental, social, and economic KPIs, with the rural site ranking highest. ML surrogates streamline AHP consistency checks and sensitivity analysis, aiding decision-making.
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
日本では、再生可能エネルギー導入拡大と地域共生が課題であり、本フレームワークは自治体や企業が複数候補地を評価する際の参考となる。MLによる評価効率化は、日本のFIT終了後の自立型再エネ事業計画にも応用可能。
In the global GX context
Globally, this work contributes to the growing literature on AI-assisted multi-criteria decision-making for renewable energy siting, relevant to ISSB-aligned sustainability reporting and transition finance. The hybrid approach supports evidence-based site selection, which is critical for meeting net-zero targets and attracting green investment.
👥 読者別の含意
🔬研究者:Provides a novel integration of ML surrogates with AHP for renewable site selection, offering a methodological template for future studies.
🏢実務担当者:Offers a practical framework for evaluating hybrid renewable projects across multiple sectors, useful for corporate sustainability teams planning decarbonization investments.
🏛政策担当者:Demonstrates a data-driven approach to prioritizing renewable energy investments, which can inform regional energy transition policies.
📄 Abstract（原文）
Hybrid renewable energy systems (HRESs) represent a promising strategy for reducing carbon emissions across multiple sectors by integrating complementary resources such as solar, wind, hydropower, and energy storage technologies. Identifying the most suitable location for a pilot installation requires a comprehensive evaluation that balances technical performance, environmental benefits, social considerations, and economic feasibility. This study employs an enhanced multi-criteria decision analysis (MCDA) framework, supported by machine learning (ML) techniques, to assess four pilot sites developed within the HY4RES project: a rural community, an aquaculture facility, a port installation, and an agriculture network. A comprehensive set of key performance indicators (KPIs) was established to capture technical, environmental, social, and economic dimensions. These include the degree of hybridization, carbon intensity, community benefit scores, net present value, levelized cost of energy, and payback period. After collecting and normalizing the site-specific data, ML EL-SVM, decision tree, and logistic regression models as computational surrogates designed to bypass the multi-step, matrix inversion mathematical requirements of the AHP when screening massive numbers of future scenario outputs supporting consistency checks and sensitivity exploration were used, along with criterion adjustments, to refine the relative importance of each KPI. The Analytical Hierarchy Process (AHP) was employed to assess potential factors and rank the sites, with the rural site achieving the highest overall score in the system, driven by its complex four-source hybrid configuration and strong community-level benefits. The agriculture scheme ranked second, demonstrating significant potential for carbon emission reductions. The port pilot placed third, distinguished by high technical innovation but more limited social impact. The aquaculture site ranked fourth, primarily due to environmental scores, despite its economic self-sufficiency.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/cleantechnol8040113first seen 2026-08-12 04:46:00
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