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2025 | OriginalPaper | Chapter

Information Extraction to Identify Novel Technologies and Trends in Renewable Energy

Authors : Connor MacLean, Denis Cavallucci

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

Publisher: 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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Title: Information Extraction to Identify Novel Technologies and Trends in Renewable Energy
Authors: Connor MacLean
Denis Cavallucci
Publisher: Springer Nature Switzerland
Book: World Conference of AI-Powered Innovation and Inventive Design
Print ISBN: 978-3-031-75922-2

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

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

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