Renewable Energy: A Sustainable Future - The Impact of Artificial Intelligence
再生可能エネルギー：持続可能な未来 - 人工知能の影響 (AI 翻訳)
Rajiv Pandey, Rohit Pradhan, Bharti Shrivas
🤖 gxceed AI 要約
日本語
本論文は、再生可能エネルギーシステムにおける人工知能（AI）の役割を評価する。LSTMモデルを用いた太陽光発電予測により、精度25%向上、グリッド効率15%向上、運用コスト30%削減を実証。AIが2050年ネットゼロ達成の鍵と結論付ける。
English
This paper evaluates the role of AI in optimizing renewable energy systems, using a deep learning LSTM model for solar power forecasting. Results show 25% improvement in forecasting accuracy, 15% grid efficiency gain, and 30% cost reduction. Concludes that AI is a key enabler for 2050 net-zero targets.
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
日本でも再生可能エネルギーの大量導入に伴う系統安定化が課題であり、AI活用の知見は国内のエネルギー転換に示唆を与える。ただし本論文は日本固有の政策や制度に言及しておらず、一般論として読むべきである。
In the global GX context
This paper adds to the global discourse on AI-enabled energy transition, demonstrating measurable benefits for grid management. It provides evidence for practitioners and policymakers considering digitalization as a cost-effective lever for renewable integration.
👥 読者別の含意
🔬研究者:Deep learning LSTM applied to solar forecasting with global datasets; useful as benchmark for AI in renewables research.
🏢実務担当者:Quantified benefits of AI for grid efficiency and cost reduction can support investment cases for digitalization.
🏛政策担当者:Highlights AI as a strategic tool for renewable energy targets; may inform policy incentives for AI adoption in energy sectors.
📄 Abstract（原文）
The global transition toward renewable energy is a cornerstone of climate change mitigation and long-term energy security. However, the inherent intermittency of solar and wind sources poses significant challenges to grid stability. This paper evaluates the transformative role of Artificial Intelligence (AI) in optimizing renewable energy systems, focusing on forecasting, grid management, and predictive maintenance. Utilizing global capacity data from IRENA and the IEA (2020-2025), we analyze the performance of a Deep Learning-based Long Short-Term Memory (LSTM) model for solar power prediction. Our results demonstrate that AI integration enhances forecasting accuracy by up to 25% and improves overall grid efficiency by 15%, while reducing operational maintenance costs by approximately 30%. The paper concludes that AI-driven digitalization is the primary catalyst for achieving 2050 net-zero targets.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.21275/sr26410115859first seen 2026-05-05 08:01:52 · last seen 2026-05-05 19:14:28
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