The path toward decarbonization in energy systems: A review on optimization, machine learning and spatiotemporal approaches
エネルギーシステムの脱炭素化への道：最適化、機械学習、時空間アプローチに関するレビュー (AI 翻訳)
George Halkos, Panagiotis-Stavros Aslanidis
🤖 gxceed AI 要約
日本語
本レビューは、エネルギーシステムの脱炭素化に向けた最適化、機械学習、時空間分析を統合した方法論を体系的に整理。2001～2024年の82論文を分析し、多目的最適化、空間分解、地理空間データ補間、計量経済・MLによる時空間予測の4つの研究ストランドに焦点を当てる。方法論的ギャップを指摘し、ネットゼロ目標下の意思決定を支援する枠組みを提供する。
English
This review systematically synthesizes optimization, machine learning, and spatiotemporal analysis methods for decarbonizing energy systems. Analyzing 82 papers (2001-2024), it identifies four research strands: multiobjective optimization, spatial decomposition, geospatial interpolation, and econometric/ML forecasting. It highlights methodological gaps and provides a framework to support decision-making under net-zero targets.
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GX政策（GX推進戦略、次期エネルギー基本計画）では、再生可能エネルギー導入拡大と系統安定化が課題。本レビューの時空間分析とML手法は、地域別の再エネポテンシャル評価や需給予測に応用可能で、SSBJ対応のScope 2排出量算定の精度向上にも寄与する。
In the global GX context
Globally, this review supports the ISSB/CSRD disclosure agenda by providing robust methods for quantifying emissions and transition risks. It offers a comprehensive methodological toolkit for energy modeling that aligns with net-zero targets, useful for policymakers and researchers addressing climate transition.
👥 読者別の含意
🔬研究者:Provides a structured overview of optimization and ML methods for energy decarbonization, highlighting gaps for future research.
🏢実務担当者:Offers a framework for selecting analytical tools to support energy transition planning and emissions reduction strategies.
🏛政策担当者:Informs evidence-based policy design for net-zero energy systems by summarizing state-of-the-art analytical approaches.
📄 Abstract（原文）
The decarbonization of energy systems requires analytical methods that combine optimization, machine learning (ML), and spatiotemporal analysis to capture the complex dynamics of energy production, distribution, and consumption used in the energy policy agenda under the transition toward a net-zero emissions energy system. The analysis is based on 82 peer-reviewed papers that cover the period 2001–2024 from Scopus, Web of Science, and Google Scholar databases. Based on the analyzed publications, the review focuses on four research strands (i) multiobjective optimization algorithms in energy modeling, (ii) spatial decomposition and environmental impact assessment techniques, (iii) geospatial data interpolation and autocorrelation analysis, and (iv) advanced econometric and ML approaches for spatiotemporal forecasting. The review offers a three-fold contribution (i) it systematically compares empirical spatiotemporal advances used to analyze decarbonization in the energy sectors, (ii) critically synthesizes spatiotemporal econometric techniques, optimization, and ML techniques in capturing spatial heterogeneity and temporal dynamics for sustainable energy development, and (iii) highlights methodological gaps relevant for energy decision-making under net-zero targets. Overall, the present review provides a holistic methodological framework that can support decarbonization efforts toward sustainable energy development.
🔗 Provenance — このレコードを発見したソース
- crossref https://doi.org/10.1177/0958305x261474851first seen 2026-08-06 06:04:15
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