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、電力網や再生可能エネルギーネットワークを含む現代のエネルギーシステムにおけるエネルギーフローの実験的モデリング手法を探求する。デジタルツインやシミュレーション環境を活用し、スマートグリッドや省エネビルなどへの応用を論じ、持続可能なエネルギーシステムへの移行を支援する。
English
This study explores experimental modeling for energy flow in modern energy systems, including power grids and renewable networks. It emphasizes digital twins and simulation platforms for smart grids, energy-efficient buildings, and intelligent transportation, supporting the transition to sustainable energy systems.
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
日本では再生可能エネルギー統合とデジタルツイン技術がGX推進の鍵であり、本論文の実験的モデリング手法は国内のスマートグリッドやエネルギーマネジメントに示唆を与える。
In the global GX context
Globally, digital twins and experimental modeling are increasingly vital for integrating renewables and optimizing energy systems. This paper contributes to the growing body of work on simulation-based decision-making for the energy transition.
👥 読者別の含意
🔬研究者:Energy system modelers can adopt the experimental modeling framework for multi-scale analysis.
🏢実務担当者:Energy companies can use digital twin concepts for system optimization and scenario testing.
🏛政策担当者:Policymakers can reference this approach to assess grid resilience and renewable integration strategies.
📄 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.20074027first seen 2026-05-26 04:35:43 · last seen 2026-05-27 04:32:13
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