スマートグリッドシステムにおける人工知能の応用と、アクセシブルで持続可能なエネルギーへの影響
Applications of Artificial Intelligence in Smart Grid Systems and Their Impact on Accessible and Sustainable Energy (原題)
Sardjono, Wahyu
🤖 gxceed AI 要約
日本語
本論文はスマートグリッドにおけるAI（機械学習・深層学習・強化学習）の応用を体系的に整理し、SDG7（安価でクリーンなエネルギー）への直接的貢献を評価する。AIによる予測モデリングが再生可能エネルギー発電予測誤差を最大25%削減し、深層強化学習による需要側管理が配電網効率と資産寿命を最適化することを示す。マルチエージェントシステムがVPPやエッジ機器の運用コスト低減と系統安定性確保に有効であることも明らかにし、電力事業者向けの実装フレームワーク（ガバナンス・サイバーセキュリティ・モデル解釈性）を提示する。
English
This paper synthesizes AI applications (machine learning, deep learning, reinforcement learning) in smart grids and maps their direct contribution to SDG 7. It finds AI-driven forecasting cuts renewable generation forecast errors by up to 25%, while deep-RL demand-side management optimizes distribution efficiency and asset lifetimes. Multi-agent systems are shown to optimize Virtual Power Plants and edge devices, lowering operating costs and ensuring grid stability, with a strategic implementation framework for utilities covering governance, cybersecurity, and model interpretability.
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う系統安定化と出力予測精度向上が喫緊の課題であり、本論文のAI活用知見は電力会社の系統運用や需給管理、VPP実証に直結する。SSBJ開示やGX推進の文脈でも、AIによる効率化が脱炭素投資の費用対効果を高める根拠として参照可能。
In the global GX context
Globally, this paper supports the operational backbone of transition finance and grid decarbonization: AI-driven forecasting and demand management are increasingly cited in utility transition plans and ISSB-aligned climate resilience disclosures. It adds a structured SDG 7 framing that complements TCFD/ISSB physical-risk and adaptation discussions for power-sector disclosure.
👥 読者別の含意
🔬研究者:Provides a structured synthesis of AI methods mapped to SDG 7 vectors, useful as a baseline for empirical validation of forecasting and DSM performance claims.
🏢実務担当者:Offers a practical implementation framework for utilities deploying AI in VPPs and demand-side management, with emphasis on governance and cybersecurity readiness.
🏛政策担当者:Highlights governance, cybersecurity, and model interpretability as prerequisites for secure AI deployment in grid modernization and energy-transition policy.
📄 Abstract（原文）
The rapid modernization of electrical grids necessitates the integration of intelligent computing technologies to handle the high volatility and bidirectional complexity of decentralized renewable energy networks. This paper provides a comprehensive analysis of Artificial Intelligence (AI) applications within modern smart grid systems and establishes their direct strategic contribution to United Nations Sustainable Development Goal 7 (SDG 7: Affordable and Clean Energy). Utilizing a systematic analytical synthesis and conceptual framework mapping approach, the study evaluates how machine learning, deep learning, and reinforcement learning optimize the three main vectors of SDG 7: universal access, increasing renewable energy shares, and doubling energy efficiency. The findings reveal that AI-driven predictive modeling reduces renewable generation forecasting errors by up to 25%, while deep-reinforcement-learning-based demand-side management optimizes distribution network efficiency and asset lifetimes. Furthermore, a detailed evaluation of multi-agent systems highlights their utility in optimizing Virtual Power Plants (VPPs) and smart grid edge devices, directly lowering operating costs and ensuring grid stability. The paper concludes with a strategic implementation framework tailored for global power utilities, highlighting governance, cyber security, and model interpretaability as critical factors for secure AI deployment in the transition to sustainable energy ecosystems.
🔗 Provenance — このレコードを発見したソース
- Zenodo https://zenodo.org/records/24144554first seen 2026-11-12 04:12:48
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