Artificial Intelligence (AI) in Renewable Energy Systems
再生可能エネルギーシステムにおける人工知能（AI） (AI 翻訳)
Nupur Mittal, ImranUllah Khan, Zohaib Hasan Khan, Farooq Ahmad, Mohd Amir Ansari
🤖 gxceed AI 要約
日本語
本論文は、再生可能エネルギー（太陽光、風力、水力、バイオエネルギー）システムにおけるAIの役割を包括的にレビューする。予測分析や機械学習モデルを通じて発電・配電の最適化、エネルギー貯蔵の向上、効率改善を実現する方法を解説。AIと再生可能エネルギーの相乗効果が持続可能なエネルギー実践を促進することを示す。
English
This chapter reviews the role of AI in renewable energy systems (solar, wind, hydro, bioenergy). It highlights how AI and ML optimize generation and distribution through predictive analytics, improve energy storage, and enhance efficiency. The synergy between AI and renewables offers pathways for sustainable energy practices.
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
日本は再生可能エネルギーの導入拡大と電力系統の安定化が課題であり、本論文のAIによる最適化手法は、日本のエネルギー転換（GX）における重要な技術的示唆を提供する。特に、変動性再生可能エネルギーの予測制御にAIを活用する点は、日本の電力システム改革に資する。
In the global GX context
Globally, the integration of AI into renewable energy systems is critical for achieving climate targets. This paper summarizes how AI can address intermittency and grid integration challenges, providing a foundation for further research and policy support in the energy transition.
👥 読者別の含意
🔬研究者:Provides a broad overview of AI applications in renewable energy, useful for identifying research gaps and interdisciplinary opportunities.
🏢実務担当者:Energy companies can leverage AI tools for optimizing renewable generation, storage, and grid integration to improve operational efficiency.
🏛政策担当者:Highlights the need for supportive policies and investments in AI to accelerate renewable energy deployment and grid modernization.
📄 Abstract（原文）
The global energy demand is anticipated to grow at an average annual rate of 8% from 2000 to 2030. Fossil fuels currently supply a significant portion of this energy, having profound impacts. Renewable energy (RE) sources are gaining popularity as alternatives to fossil fuels for various reasons. However, the transition to RE sources is critical for addressing climate change and reducing dependence on fossil fuels. However, the variability and intermittency of RE present significant challenges for efficient utilization and integration into power grids. Artificial intelligence (AI) and machine learning (ML) are relatively new concepts in the energy sector. The proposed work highlights how AI can optimize the generation and distribution of RE through predictive analytics and ML models. This chapter explores the role of AI in enhancing the efficiency and reliability of RE systems (RES), including solar, wind, hydro, and bioenergy. The work explores the contribution of AI to improving energy storage. The proposed work emphasizes the role of AI in improving energy efficiency and conservation. The synergy between AI and RES offers innovative pathways to conserve resources and promote sustainable energy practices.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1201/9781003544388-19first seen 2026-07-24 05:30:18
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