The impact of digital technology on the evolution of renewable energy sources
デジタル技術が再生可能エネルギー源の進化に与える影響 (AI 翻訳)
Reza Hafezi, Saeed Roshani, Mahsa Rajabzadeh, Mehdi Majidpour
🤖 gxceed AI 要約
日本語
本研究は、2000年から2023年までのWeb of Scienceの2万件以上の文献をSciBERTトピックモデリングとクラスタリングで分析し、再生可能エネルギー分野におけるデジタル技術の統合パターンを10のクラスタに分類した。生産からサービス指向、デジタルデバイスから知的計算手法に至る領域を横断し、研究テーマの分布と将来の技術発展方向を示す。
English
This study analyzes over 20,000 articles from Web of Science (2000-2023) using SciBERT topic modeling and clustering to map digital technology integration in renewable energy. It identifies ten clusters spanning production-to-service and device-to-intelligent methods, offering a structured overview of research themes and future directions.
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政策では、再生可能エネルギーの導入拡大とデジタル技術の活用が重要課題であり、本分析は研究動向の俯瞰を提供する。ただし、具体的な政策や企業実務への示唆は限定的で、日本の文脈への直接的な関連性は中程度。
In the global GX context
Globally, this paper contributes to understanding the digitalization of renewable energy, a key aspect of the energy transition. It provides a systematic mapping that can inform research agendas and stakeholder engagement, though it does not address specific policy or disclosure frameworks.
👥 読者別の含意
🔬研究者:Provides a comprehensive map of digitalization in renewable energy research, useful for identifying research gaps and trends.
🏢実務担当者:Offers a structured overview of digital technology applications in renewable energy, potentially guiding technology adoption strategies.
📄 Abstract(原文)
Purpose The integration of digital technologies into renewable energy systems is rapidly transforming the landscape of power generation, offering new pathways for enhancing efficiency, sustainability and scalability. This paper aims to examine the evolving role of digitalization in the development of renewable energy. Design/methodology/approach The study employs a comprehensive two-phase methodology, comprising machine-based pattern recognition and human-based interpretation phases. In the initial phase of the study, the Web of Science database was queried to identify relevant literature published between 2000 and 2023. This search yielded a dataset of over 20,000 articles. The findings were employed to analyze the thematic structures and technological trajectories. Novel text-mining techniques were employed, including SciBERT-based topic modeling, dimensionality reduction and K-means clustering. Findings The findings highlight broad patterns in the way digital technologies are incorporated into renewable energy research and point to emerging areas of interest across the field. The analysis identifies ten clusters that map the intersections between digital transformation and renewable energy across different domains. These domains span production- to service-oriented activities on one axis and digital devices to intelligent computational methods on the other. The results provide a structured overview of how research themes are distributed and suggest possible directions for future technological development and stakeholder engagement. Research limitations/implications The analysis is based on titles, abstracts and keywords, which capture thematic patterns but not the full methodological depth of individual studies. In addition, the dataset is drawn exclusively from the Web of Science database, which may exclude relevant publications indexed elsewhere. Originality/value This paper offers a systematic methodological framework for mapping technology evolution in the context of renewable energy digitalization and provides a structured interpretation of how digital tools appear across different research domains. The study contributes to the growing body of work at the intersection of digital innovation and sustainable energy by presenting a large-scale, evidence-based overview of current research trends and their potential implications for future development pathways.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1108/fs-03-2025-0046first seen 2026-07-31 05:14:48
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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。