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2025 | OriginalPaper | Buchkapitel

Information Extraction to Identify Novel Technologies and Trends in Renewable Energy

verfasst von : Connor MacLean, Denis Cavallucci

Erschienen in: World Conference of AI-Powered Innovation and Inventive Design

Verlag: Springer Nature Switzerland

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Abstract

Achieving carbon neutrality by 2050 requires unprecedented technological, economic, and sociological changes. With time as a scarce resource, it is crucial to base decisions on relevant facts and information to avoid misdirection. This study aims to help decision makers quickly find relevant information related to companies and organizations in the renewable energy sector. Over the course of this PhD program, we will propose several text-mining methods applied to the renewable energy sector in order to detect technological breakthroughs and new, innovative companies. These techniques include specialized Named Entity Recognition (NER) models, news summarization, and trend analysis of scientific articles. Further steps in this project will contain a TRIZ-based analysis of scientific articles in order to attribute a multi-factor score on the innovative potential of novel technologies.

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Vorheriges Kapitel Leveraging Information Retrieval Pipelines for Inventive Design: Application in Efficient Lattice Structures Manufacturing

Nächstes Kapitel Dynamic Truth: Immutable Humans, AI and Innovation

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Titel: Information Extraction to Identify Novel Technologies and Trends in Renewable Energy
verfasst von: Connor MacLean
Denis Cavallucci
Verlag: Springer Nature Switzerland
Buch: World Conference of AI-Powered Innovation and Inventive Design
Print ISBN: 978-3-031-75922-2

Electronic ISBN: 978-3-031-75923-9

Copyright-Jahr: 2025
DOI: https://doi.org/10.1007/978-3-031-75923-9_22

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