ARTIFICIAL INTELLIGENCE-DRIVEN ENERGY MANAGEMENT SYSTEMS FOR SUSTAINABLE DECARBONIZATION: OPPORTUNITIES, CHALLENGES, AND FUTURE DIRECTIONS
人工知能駆動型エネルギー管理システムによる持続可能な脱炭素化：機会、課題、今後の方向性 (AI 翻訳)
Mohammed Abdalghafoor, IJETRM Journal
🤖 gxceed AI 要約
日本語
本レビューは、AIを活用したエネルギー管理システム（EMS）が持続可能な脱炭素化にどのように貢献できるかを包括的に分析。機械学習、深層学習、強化学習、予測分析、デジタルツインなどの最新技術を概観し、リアルタイム最適化、需要予測、グリッドレジリエンス、排出削減の機会を提示。一方で、データ品質、サイバーセキュリティ、モデルの透明性、スケーラビリティ、規制遵守などの課題も指摘。説明可能なAI、エッジインテリジェンス、連合学習、政策枠組みなど今後の研究方向性を示す。
English
This review comprehensively analyzes how AI-powered Energy Management Systems (EMS) can contribute to sustainable decarbonization. It surveys recent advances in machine learning, deep learning, reinforcement learning, predictive analytics, and digital twins, highlighting opportunities in real-time optimization, demand forecasting, grid resilience, and emission reduction. Key challenges including data quality, cybersecurity, model transparency, scalability, and regulatory compliance are discussed. Future research directions such as explainable AI, edge intelligence, federated learning, and holistic policy frameworks are identified.
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
日本は2050年カーボンニュートラルを目標としており、AIによるEMSは再生可能エネルギー統合や需要側管理に有用。本レビューは日本のエネルギーシステムの最適化や政策立案にも示唆を与える。
In the global GX context
Globally, AI-driven EMS is a key enabler for the energy transition. This review provides a structured overview of opportunities and challenges relevant to any country pursuing decarbonization, including those with aggressive renewable targets and grid modernization efforts.
👥 読者別の含意
🔬研究者:Identifies key research gaps and future directions in AI for energy management, such as explainable AI and federated learning.
🏢実務担当者:Highlights practical opportunities for real-time optimization and demand forecasting that can reduce operational costs and emissions.
🏛政策担当者:Emphasizes regulatory compliance and the need for holistic policy frameworks to support AI adoption in energy systems.
📄 Abstract（原文）
The shift to carbon neutrality has speeded up in the world, and the use of intelligent energy management systems that optimize energy production, energy distribution, storage and energy consumption have increased. Artificial Intelligence (AI) is a game-changer for boosting efficiency, integrating renewables, and guiding energy sector decision-making in today's energy systems. The review explores the potential of AI-based Energy Management Systems (EMSs) to aid in the progress of sustainable decarbonization, summarizing recent advances in machine learning, deep learning, reinforcement learning, predictive analytics, and digital twin technologies. It explores significant opportunities such as real-time energy optimization, demand forecasting, grid resilience, and reduction of emissions, along with the critical challenges of data quality, cybersecurity, transparency of models, scalability and regulatory compliance. The review also identifies future research avenues, particularly focused on explainable AI, edge intelligence, federated learning, and holistic policy frameworks, to propel the creation of secure, efficient, and sustainable low-carbon energy ecosystems.
🔗 Provenance — このレコードを発見したソース
- Zenodo https://zenodo.org/records/21312392first seen 2026-07-12 04:11:47 · last seen 2026-07-12 04:16:36
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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。