D2.2 Advanced Forecasting Tools
D2.2 高度な予測ツール (AI 翻訳)
Lombardi, Pio Alessandro, Arendarski, Bartlomiej, Sikorski, Tomasz, ZIZZO, Gaetano, Cannizzaro, Francesco Saverio, Chudzik, Krystian, Smagowski, Szymon, Suresh, Vishnu, Aksan, Fachrizal Fajrin, Witkowski, Mateusz
🤖 gxceed AI 要約
日本語
本論文はFlexBITプロジェクトで開発された太陽光発電と電力需要の超短期・短期予測ツールを報告する。機械学習とスケーラブルなデータ処理を組み合わせ、ミニットレベルの予測を実現し、プラットフォームのリアルタイム制御と最適化を支援する。再生可能エネルギーの統合と柔軟性管理に貢献する。
English
This deliverable presents advanced forecasting tools for ultra-short-term and short-term prediction of PV generation and electricity demand within the FlexBIT project. Using machine learning and scalable data pipelines, it provides minute-level predictions to support real-time control and optimization of energy systems. The tools enhance renewable energy integration and flexibility management across residential, tertiary, and industrial sectors.
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
本論文は欧州のFlexBITプロジェクトにおける予測技術に焦点を当てており、日本のGX文脈に直接関連しないが、太陽光発電予測の高度化手法は日本の再生可能エネルギー統合において参考になる。
In the global GX context
This paper provides technical details on advanced forecasting for PV and demand, supporting renewable energy integration and flexibility management. While not directly tied to global disclosure frameworks, it contributes to the operational tools needed for effective energy transition. It is relevant for researchers and practitioners working on real-time control and optimization of distributed energy resources.
👥 読者別の含意
🔬研究者:This paper offers detailed methodologies for ultra-short-term PV and load forecasting using machine learning, useful for researchers in renewable energy integration and smart grid control.
🏢実務担当者:The forecasting tools described can inform the development of real-time energy management systems for distributed resources, but may require adaptation for specific operational contexts.
📄 Abstract（原文）
This deliverable presents the advanced forecasting tools developed within the FlexBIT project, focusing on ultra-short-term and short-term prediction of photovoltaic (PV) generation and electricity demand. The forecasting module constitutes a key component of the FlexBIT digital platform, providing predictive capabilities that support real-time control, optimization, and flexibility management across residential, tertiary, and industrial energy systems. The scope of this deliverable covers data originating from multiple sources, including platform operational data, IoT and SCADA/EMACS measurements, as well as external data streams such as weather services and market signals. The document describes the data sources, feature engineering techniques, forecasting methodologies, model selection strategies, and validation procedures applied across different demonstrators within the project. The forecasting tools are designed to operate under the specific requirements of the FlexBIT platform, where high temporal resolution, low latency, and robustness to variability are essential. In particular, the models support rolling predictions at minute-level granularity, enabling their integration into the real-time advisory and control loop of the platform. This deliverable builds upon the system architecture and control concepts defined in Deliverable D2.1, providing the predictive layer that feeds optimization and decision-making modules. It is closely linked with other activities within Work Package 2, including data integration, flexibility identification, and the development of control algorithms. By combining advanced machine learning techniques with scalable data processing pipelines, the forecasting module contributes to the overall objectives of FlexBIT, enabling improved utilization of renewable energy sources, enhanced
🔗 Provenance — このレコードを発見したソース
- Zenodo https://zenodo.org/records/20506602first seen 2026-06-03 04:26:18 · last seen 2026-06-03 04:26:20
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