Renewable Energy Transition in India: Role of Artificial Intelligence in Optimising Renewable Energy Generation and Distribution
インドにおける再生可能エネルギー転換：再生可能エネルギーの発電と配電を最適化する人工知能の役割 (AI 翻訳)
Dr. Sweety Supriya
🤖 gxceed AI 要約
日本語
本レビュー論文は、インドの再生可能エネルギー導入におけるAIの役割を探る。気候変動対策として再エネが重視される中、AIは出力変動の予測や蓄電管理、系統安定化に貢献できる。既存研究の整理から、AI・機械学習技術が再エネ発電と配電の最適化に大きな可能性を持つと結論づけている。
English
This review paper explores the role of Artificial Intelligence in India's renewable energy transition. It finds that AI can address intermittency, improve forecasting, and enhance grid stability. The paper concludes that AI and machine learning hold significant potential for optimizing renewable energy generation and distribution.
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
インドを対象とするが、日本でも再エネ大量導入時の系統安定化や需給予測にAI活用が進んでおり、類似課題への示唆となる。
In the global GX context
This paper provides a broad review of AI applications for renewable energy optimization, relevant to global discussions on grid integration and smart energy systems.
👥 読者別の含意
🔬研究者:A useful overview of current AI applications in renewable energy, highlighting key pathways and limitations for future research.
🏢実務担当者:Energy companies can gain insights into how AI improves forecasting and storage management for renewable integration.
🏛政策担当者:Highlights policy considerations for promoting AI deployment in renewable energy systems, relevant to national climate action plans.
📄 Abstract（原文）
Abstract: India is prioritising the deployment of renewable energy as a central pillar of its sustainable development policy and climate action plan. This shift towards renewable energy systems presents complex operational constraints arising from the intermittency of renewable energy sources, information asymmetries in forecasting power requirements, and the need for smart and robust energy infrastructure. In this context, this review paper aims to explore the evolving role and potential of Artificial Intelligence (AI) in facilitating sustainable energy transitions. Drawing on interdisciplinary literature, this paper explores the application of AI to data-driven decision-making to enhance renewable energy forecasting and intelligent energy storage management, thereby improving grid stability. Further, by drawing on theoretical and empirical insights, the paper seeks to contribute to the identification of key pathways, limitations, and policy-oriented considerations for shaping the future deployment of AI in sustainable energy production and distribution. The paper finds that recent developments in AI models and machine learning-based technologies, and their deployment in the renewable energy ecosystem, hold great potential for advancing renewable energy generation and distribution.
🔗 Provenance — このレコードを発見したソース
- Zenodo https://zenodo.org/records/20407319first seen 2026-05-28 04:14:54 · last seen 2026-06-04 04:25:24
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