EXPERIMENTAL MODELING OF ENERGY FLOW AND SYSTEM PLATFORMS
エネルギーフローとシステムプラットフォームの実験的モデリング (AI 翻訳)
Umudov Ismail Iman oglu
🤖 gxceed AI 要約
日本語
本研究は、スマートグリッドや再生可能エネルギー統合におけるエネルギーシステムの実験的モデリング手法を探求する。特に、デジタルツインやシミュレーション環境を活用し、非線形・多規模特性を捉える方法論を提案。データの不均質性や複雑性といった課題にも言及し、持続可能なエネルギーシステム構築への貢献を目指す。
English
This study explores experimental modeling methods for energy systems, focusing on smart grids and renewable integration. It emphasizes digital twins and simulation to capture nonlinear, multi-scale characteristics. Challenges like data heterogeneity and system complexity are addressed, aiming to support sustainable energy system development.
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、再生可能エネルギー統合やスマートグリッドの最適化に役立つ可能性がある。ただし、具体的な日本の政策や規制（例：FIT、グリーン成長戦略）との関連性は明示されていない。
In the global GX context
Globally, this paper contributes to the discourse on digital twin applications for energy systems, aligning with trends in smart grid optimization and renewable integration. However, it lacks novelty in terms of empirical findings or policy implications.
👥 読者別の含意
🔬研究者:エネルギーフローの実験的モデリング手法に関する知見を提供。
🏢実務担当者:デジタルツイン技術を活用したエネルギーシステム最適化の可能性を示す。
🏛政策担当者:スマートグリッドや再生可能エネルギー統合に向けたモデリングの重要性を強調。
📄 Abstract（原文）
Energy flow constitutes a fundamental dimension of modern engineering systems, encompassing power grids, renewable energy networks, industrial infrastructures, and cyber-physical platforms. As these systems become increasingly complex due to digitalization, decentralization, and the integration of intelligent technologies, there is a growing need for advanced methods to analyze, predict, and optimize their behavior. This study explores the conceptual foundations and practical methodologies of experimental modeling, emphasizing its role in capturing the nonlinear, multi-scale, and adaptive characteristics of contemporary energy systems. Particular attention is given to system platforms that integrate physical components (such as energy generation and storage units), data acquisition technologies (including sensors and monitoring systems), and advanced analytics (such as machine learning and real-time optimization algorithms). These platforms facilitate the creation of digital twins and simulation environments, allowing for precise experimentation, scenario analysis, and performance evaluation. Furthermore, the article highlights key applications of experimental modeling in areas such as smart grids, renewable energy integration, energy-efficient buildings, and intelligent transportation systems. It also discusses current challenges, including data heterogeneity, system complexity, and the need for scalable and cost-effective solutions. By addressing these issues, experimental modeling contributes to the development of resilient, efficient, and sustainable energy systems. Overall, this research underscores the significance of integrating experimental approaches with digital technologies to enhance decision-making, improve system reliability, and support the transition toward a more sustainable and energy-efficient future.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.5281/zenodo.20074026first seen 2026-05-28 04:42:58 · last seen 2026-06-06 04:35:03
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