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Energy Data: Overview Paper – Cluster 1

エネルギーデータ：概要論文 – クラスター1 (AI 翻訳)

Oskay Ozen, Niklas Panten, Nils Roloff, Armin Shalile, Matthias Weigold

TUprintsジャーナル2026-04-27#エネルギー転換Origin: EU
DOI: 10.26083/tuda-8012
原典: https://doi.org/10.26083/tuda-8012

🤖 gxceed AI 要約

日本語

本論文は、ドイツのプロジェクトにおける分散型エネルギーシステムのためのICT基盤開発を概説する。スマートメーター、IoT、機械学習を活用し、データ収集・分析による最適化と動的価格設定を実現する。これにより都市のエネルギー柔軟性向上と科学的根拠の提供を目指す。

English

This paper outlines a German project developing an ICT infrastructure for decentralized energy systems. It uses smart meters, IoT, and machine learning for data collection, analysis, optimization, and dynamic pricing, aiming to enhance urban energy flexibility and provide a scientific basis for cross-sectoral operational strategies in the energy transition.

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

本論文はドイツのエネルギー転換プロジェクトを対象としており、日本でも分散型エネルギーシステムのデータ基盤構築に示唆を与える。

In the global GX context

This paper presents a German project on ICT infrastructure for decentralized energy systems, relevant to global efforts in energy data management and grid integration.

👥 読者別の含意

🔬研究者:Researchers in energy informatics and smart grid data infrastructure can learn from the project's multi-layered approach to data aggregation and optimization.

🏢実務担当者:Corporate sustainability teams implementing energy management systems can gain insights into ICT architectures for integrating decentralized generation and demand response.

🏛政策担当者:Policymakers can note the role of dynamic pricing and data transparency in enabling energy flexibility and grid stability.

📄 Abstract（原文）

The ongoing energy transition necessitates a transformation of existing infrastructures toward decentralized and cross-sectorally integrated energy systems. While current practices are domi-nated by locally oriented control logics with simplified operational rules, the project aims to estab-lish a superordinate coordination of generation, storage, and consumption units through the de-ployment of advanced sensor technology, digital networking, and data-driven analytical methods. A central component of the project is the development of an ICT infrastructure that enables the collection, aggregation, and evaluation of decentrally generated data. Both advanced technolo-gies, such as smart meters, LoRaWAN, and IoT solutions, as well as machine learning methods are employed. The resulting data transparency provides the foundation for the development and implementation of optimization strategies that account for ecolonomical and grid-related objec-tives. Cluster 1 of the project addresses these objectives through five key measures: the establishment of a scalable platform infrastructure, the synthesis and visualization of sensor data, data-driven analysis and optimization, and the design of dynamic pricing and billing mechanisms. Overall, the project makes a significant contribution to enhancing the energy flexibility of urban systems and to providing a scientific basis for cross-sectoral operational strategies in the context of the energy transition.

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