再生可能エネルギーシステム最適化における人工知能の役割
The Role of Artificial Intelligence in Optimizing Renewable Energy System (原題)
Shereef Pelumi Gbadamosi
🤖 gxceed AI 要約
日本語
本論文は、太陽光・風力・水力・地熱などの再エネが持つ間欠性への対応として、AIの活用可能性を文献レビューで検討する。機械学習・深層学習・強化学習が発電量予測、スマートグリッドのリアルタイム運用、予知保全、故障検知の精度を高め、需給調整や系統信頼性の向上、廃棄ロスの削減、運用コスト低減に寄与することを示す。システム理論・最適化理論・技術決定論を枠組みとし、実装コストやデータプライバシー、インフラ互換性といった課題も指摘する。
English
This literature-based qualitative study examines how AI—machine learning, deep learning, and reinforcement learning—optimizes renewable energy systems. It finds AI improves solar and wind forecasting, real-time smart-grid management, fault detection, and predictive maintenance, enabling better supply-demand balancing, lower waste, and reduced operating costs. Grounded in systems, optimization, and technological determinism theories, it also flags high implementation costs, data privacy, and infrastructure compatibility as barriers.
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
日本では再エネ大量導入に伴う系統安定化と出力予測高度化が喫緊の課題であり、第7次エネルギー基本計画やGX推進の文脈でAI活用の示唆は大きい。ただしESG開示やSSBJ対応への直接的示唆は乏しく、実務家には限定的な参考情報にとどまる。
In the global GX context
Globally, this fits the grid-integration and energy-transition strand of GX rather than disclosure frameworks like TCFD/ISSB/CSRD. It offers a structured overview of AI's role in renewable optimization but adds little to climate-disclosure scholarship.
👥 読者別の含意
🔬研究者:AI×再エネ最適化の理論枠組みと研究ギャップの整理に有用。
🏢実務担当者:再エネ調達や系統運用におけるAI導入の効果と課題の把握に使える。
🏛政策担当者:系統安定化・出力予測高度化に向けたAI投資支援の根拠として参照可能。
📄 Abstract（原文）
The growing transition to renewable energy sources, such as solar, wind, hydro, and geothermal, presents significant challenges due to their intermittent nature, requiring advanced optimization strategies for efficient integration into existing power systems. Artificial Intelligence (AI) has emerged as a transformative tool in addressing these challenges by enhancing energy forecasting, optimizing smart grid operations, and improving predictive maintenance. This study employs a secondary research methodology, conducting a qualitative analysis of existing literature, case studies, and technical reports on AI applications in renewable energy optimization. The study explores how machine learning (ML), deep learning (DL), and reinforcement learning (RL) enhance energy forecasting, real-time grid management, and fault detection. AI-driven models improve the accuracy of solar and wind energy predictions, allowing better supply-demand balancing. Additionally, AI-powered smart grids enhance energy distribution, reduce wastage, and increase grid reliability. AI also enables predictive maintenance, minimizing operational costs and infrastructure failures. The research is grounded in Systems Theory, Optimization Theory, and Technological Determinism, offering a structured framework for analyzing AI’ s impact on renewable energy systems. Despite its advantages, AI integration faces challenges such as high implementation costs, data privacy concerns, and infrastructure compatibility issues. By reviewing real-world case studies, the study demonstrates AI’ s effectiveness in optimizing renewable energy systems, emphasizing its potential to accelerate the transition toward a more sustainable and resilient energy future.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.56201/ijemt.vol.11.no3.2025.pg209.229first seen 2026-11-01 04:43:29 · last seen 2026-11-06 04:36:18
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