A Review on Smart Grid Optimization Using Artificial Intelligence and Machine Learning
人工知能と機械学習を用いたスマートグリッド最適化に関するレビュー (AI 翻訳)
Charvi Goel, Mamta Rani, Rakhi Kamra
🤖 gxceed AI 要約
日本語
本レビューは、持続可能で信頼性の高いエネルギーシステムへの移行に不可欠なスマートグリッド最適化におけるAI・機械学習技術の応用を包括的に分析する。負荷予測、安定性評価、故障検出、サイバーセキュリティなど主要分野をカバーし、デジタルツインや連合学習などの最新動向も考察する。研究ギャップとしてスケーラビリティ、データプライバシー、リアルタイム展開を指摘し、今後の方向性を示す。
English
This comprehensive review analyzes the application of AI and machine learning in smart grid optimization, essential for transitioning to sustainable energy systems. It covers key areas including load forecasting, stability assessment, fault detection, and cybersecurity, and discusses recent advancements such as digital twins and federated learning. The paper identifies research gaps in scalability, data privacy, and real-time deployment, highlighting future directions for resilient energy systems.
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の大量導入に伴い、スマートグリッドの最適化が急務となっている。本レビューはAI・ML技術の適用事例を網羅的に示し、電力系統の安定化・効率化に資する知見を提供する。
In the global GX context
Smart grid optimization is a key enabler for global energy transition, especially with high renewable penetration. This review summarizes AI/ML techniques that can improve grid reliability and efficiency, relevant to utilities and system operators worldwide.
👥 読者別の含意
🔬研究者:Provides a structured overview of AI/ML applications and research gaps for researchers in smart grid and energy systems.
🏢実務担当者:Offers insights into practical AI/ML tools for grid optimization, useful for utility engineers and energy managers.
🏛政策担当者:Highlights the potential of AI in enabling smart grids, informing policy support for digitalization and grid modernization.
📄 Abstract（原文）
The transition from conventional power systems to intelligent smart grids has become essential due to the increasing demand for sustainable, reliable, and efficient energy systems. Smart grids integrate advanced communication, control, and computational technologies to enable real-time monitoring, bidirectional power flow, and automated decision-making. However, traditional optimization and control techniques are inadequate for handling the complexity, scale, and uncertainty associated with modern power systems. Artificial Intelligence (AI) and Machine Learning (ML) have emerged as key enablers for smart grid optimization, offering advanced capabilities such as predictive analytics, adaptive control, and real-time decision-making. These techniques are particularly crucial in addressing challenges introduced by the integration of renewable energy sources, which are inherently intermittent and unpredictable. This paper presents a comprehensive review of AI and ML techniques applied to smart grid optimization, focusing on key application areas including load forecasting, stability assessment, fault detection, and cybersecurity. Furthermore, recent advancements such as digital twins, federated learning, and hybrid AI models are discussed. The paper also identifies critical research gaps related to scalability, data privacy, and real-time deployment, and highlights future directions for achieving fully autonomous and resilient energy systems.
🔗 Provenance — このレコードを発見したソース
- Zenodo https://zenodo.org/records/21056630first seen 2026-07-01 04:17:24 · last seen 2026-07-01 04:17:49
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