AI-optimized renewable energy forecasting for U.S. power grids
AI最適化による米国電力網向け再生可能エネルギー予測 (AI 翻訳)
Ishmael Jesse Narh Adikorley, Eunice Abena Lettu
🤖 gxceed AI 要約
日本語
本研究は、AIと機械学習を活用した再生可能エネルギーの発電予測とスマートグリッド統合を検討。従来の時系列手法とMLを組み合わせることで予測精度が向上し、再生可能エネルギーの依存度を高められることを示した。AIベースのシステムが系統性能を改善し、炭素排出削減とエネルギーアクセス向上に寄与すると結論づけている。政策立案にも示唆を与える。
English
This study explores AI and machine learning techniques for forecasting renewable energy generation and integrating it into smart grids. By combining traditional time-series methods with ML, forecasting accuracy improves, enabling higher reliance on renewables. The findings indicate AI-based systems can enhance grid performance, reduce carbon footprints, and improve energy access, with implications for policy.
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
日本のGX文脈では、太陽光や風力の変動性への対応が課題であり、本論文のAI予測手法は日本の電力会社や系統運用者にとって応用可能性がある。特に、FIT後の市場統合や需給調整に資する技術として注目される。
In the global GX context
Globally, this paper contributes to the growing body of work on AI for energy transition, offering a methodology applicable to grid operators and policymakers aiming to increase renewable penetration. While not directly about disclosure, it supports strategic decarbonization planning relevant to TCFD and ISSB frameworks.
👥 読者別の含意
🔬研究者:Provides a comparative evaluation of AI methods for renewable forecasting, useful for further methodological refinement.
🏢実務担当者:Offers practical insights into deploying AI for better grid integration of renewables, aiding operational decisions.
🏛政策担当者:Demonstrates AI's role in enabling clean energy transitions, informing policy on smart grid investments and renewable targets.
📄 Abstract（原文）
Renewable energy has emerged as a critical component in the global pursuit of sustainable development and carbon neutrality. Despite its potential, the inherent challenges associated with renewable energy sources, such as intermittency, variability, and storage limitations, necessitate innovative solutions to enhance efficiency and reliability. The growing world demand for energy requires the incorporation of renewable energy into smart grids to create effective and efficient power systems. Through the utilization of sophisticated machine learning and combining traditional time-series methods and machine learning model tools, we conclude that the use of AI facilitates a speedy generation of better forecasting and dependency on renewable energy resources. As the demand for energy in the world continues to grow, the integration of renewable energy into smart grids has become essential for building efficient and sustainable power grid networks. In the study, artificial intelligence has become a transformative tool. Using machine learning techniques along with traditional techniques has promoted greater confidence and dependency on renewable energy resources. Overall, our findings support the statement that AI-based renewable energy systems can help integrate the transition to more sustainable energy resources by enhancing grid performance, reducing carbon footprints, and improving energy access. This study also reveals the significant role of AI in enhancing global sustainable goals for energy systems. This research also contributes to policymaking by evaluating AI’s potential in shaping sustainable energy strategies, ensuring a reliable transition to clean energy.
🔗 Provenance — このレコードを発見したソース
- crossref https://doi.org/10.30574/msarr.2026.16.2.0057first seen 2026-05-14 23:34:21
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gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。