Accelerating open innovation in wind energy: the WeDoWind EAWE Test Turbines Committee Space
風力発電におけるオープンイノベーションの加速：WeDoWind EAWEテストタービン委員会スペース (AI 翻訳)
Barber, Sarah, Clerc, Alex, Matthews, Peter, Klein, Korin, Johansson, Håkan
🤖 gxceed AI 要約
日本語
この論文は、風力発電セクターにおけるデジタル化とデータ共有の重要性を論じ、WeDoWindオープンイノベーションエコシステムを紹介する。特にEAWEテストタービン委員会スペースとデータサイエンスチャレンジを通じて、オープンイノベーションとオープンデータの実践を促進する方法を示す。これにより、風力エネルギーのコスト削減と価値向上が期待される。
English
This paper discusses the importance of digitalization and data sharing in wind energy, introducing the WeDoWind open innovation ecosystem. It presents the EAWE Test Turbines Committee Space and Data Science Challenge, demonstrating how collaborative platforms can accelerate open innovation, open science, and open data practices, reducing costs and increasing value in wind energy.
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
この研究は風力発電のデジタル変革におけるオープンイノベーションの枠組みを提供し、日本における再エネ導入促進やデータ共有の取り組みに参考となる。日本のGX政策（再エネ主力電源化）に向けたデータ基盤構築に示唆を与える可能性がある。
In the global GX context
This paper provides a framework for open innovation in wind energy digitalization, relevant for global GX efforts to accelerate renewable energy deployment through data sharing and interoperability. It aligns with trends like the IEA's focus on digitalization for clean energy transitions.
👥 読者別の含意
🔬研究者:This paper offers insights into digital platforms for open innovation in wind energy, useful for researchers studying collaborative data sharing and AI-ready information models.
🏢実務担当者:Wind energy companies can learn from the WeDoWind ecosystem to enhance data interoperability and participate in open challenges to reduce costs.
🏛政策担当者:Policymakers can consider supporting open innovation platforms to accelerate digital transformation in renewable energy sectors.
📄 Abstract（原文）
Digitalisation has emerged as a “megatrend” in wind energy [1], accelerated by advances in AI technologies and Intelligent Digital Twins [2, 3]. These developments highlight the growing need for interoperability and data sharing across stakeholders. Common, community-adopted information models make data AI-ready by providing a shared vocabulary that defines relationships and semantics, allowing machines to interpret and integrate information consistently. Open innovation [4] is vital for addressing the complex challenges of the energy transition [5] and can be enabled through digital platforms that overcome geographical and organisational boundaries [6]. Such platforms support knowledge exchange in global networks. In wind energy, the WeDoWind open innovation ecosystem provides such a platform, connecting diverse stakeholders to share data, knowledge, and experience openly [4]. WeDoWind brings together four overlapping communities: (1) Information Modelling Community – developing data models, schemas, taxonomies, and ontologies; (2) Collaborative Problem-Solving Community – exchanging knowledge through WeDoWind challenges and sharing teaching materials; (3) Open Data and Code Community – creating guidelines and best practices for publishing open code and data; (4) Data Users’ Community – developing resources and best practices for applying data and code. Several WeDoWind challenges have already been documented in the literature, showing the ecosystem’s potential to reduce costs and increase the value of wind energy [4, 7, 8]. This work introduces the WeDoWind EAWE Test Turbines Committee Space, which hosts new datasets from EAWE Test Turbines Committee members alongside the EAWE Data Science Challenge 2024–2025. We show how these initiatives strengthen open innovation, open science, and open data practices in wind energy, demonstrating how collaborative digital platforms can accelerate the sector’s digital transformation.
🔗 Provenance — このレコードを発見したソース
- Zenodo https://zenodo.org/records/20556832first seen 2026-06-06 04:14:49 · last seen 2026-06-08 04:13:59
🔔 こうした論文の新着を逃したくない方は キーワードアラート に登録（無料・3キーワードまで）。
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。