OPTIMIZATION OF RENEWABLE ENERGY SYSTEMS USING MACHINE LEARNING ALGORITHMS
機械学習アルゴリズムを用いた再生可能エネルギーシステムの最適化 (AI 翻訳)
Wilson, R. T.
🤖 gxceed AI 要約
日本語
本稿は、機械学習を用いた再生可能エネルギーシステム最適化のレビュー論文である。太陽光・風力発電の予測、蓄電最適化、系統安定化における機械学習の有効性を評価し、技術的・実用的意義を整理している。
English
This review evaluates machine learning optimization of renewable energy systems, focusing on solar/wind forecasting, storage optimization, and grid integration. It highlights improved efficiency, reliability, and sustainability, serving as a practical reference for engineers.
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
日本でも再エネ導入拡大に伴い、系統安定化や蓄電制御の高度化が急務である。本稿は基礎的な技術整理として参考になるが、日本の政策・規制（FIT/FIP等）との接点は乏しい。
In the global GX context
Globally, ML-based optimization is increasingly critical for renewable integration. This paper offers a broad technical overview, but lacks specific case studies or policy alignment with frameworks like RE100 or IRENA.
👥 読者別の含意
🔬研究者:Provides a structured overview of ML applications in renewable energy optimization, useful as an entry point for literature review.
🏢実務担当者:Offers high-level insights on ML techniques for forecasting and storage, but lacks implementation details.
📄 Abstract（原文）
The article analyzes optimization of renewable energy systems using machine learning algorithms. Machine learning improves renewable energy system performance by forecasting energy production, optimizing storage, and balancing variable generation. Intelligent algorithms support more stable integration of solar and wind resources into modern power systems. The aim of the study was to evaluate the technical, functional, and practical significance of this approach in modern engineering systems. The study used analytical review, comparative assessment, and synthesis of current engineering literature. The results show that the investigated technology improves operational efficiency, reliability, safety, and sustainability. The findings may be useful for engineers, researchers, and managers involved in the modernization of industrial and technological processes.
🔗 Provenance — このレコードを発見したソース
- Zenodo https://zenodo.org/records/20441875first seen 2026-05-30 04:26:32 · last seen 2026-05-31 04:14:04
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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。