Preprints
https://doi.org/10.5194/wes-2023-173
https://doi.org/10.5194/wes-2023-173
03 Jan 2024
 | 03 Jan 2024
Status: a revised version of this preprint was accepted for the journal WES and is expected to appear here in due course.

Knowledge Engineering for Wind Energy

Yuriy Marykovskiy, Thomas Clark, Justin Day, Marcus Wiens, Charles Henderson, Julian Quick, Imad Abdallah, Anna Maria Sempreviva, Jean-Paul Calbimonte, Eleni Chatzi, and Sarah Barber

Abstract. With the rapid evolution of the wind energy sector, there is an ever-increasing need to create value from the vast amounts of data made available both from within the domain, as well as from other sectors. This article addresses the challenges faced by wind energy domain experts in converting data into domain knowledge, connecting and integrating it with other sources of knowledge, and making it available for use in next-generation artificial intelligence systems. To this end, this article highlights the role that knowledge engineering can play in the digital transformation of the wind energy sector. It presents the main concepts underpinning Knowledge-Based Systems and summarises previous work in the areas of knowledge engineering and knowledge representation in a manner that is relevant and accessible to wind energy domain experts. A systematic analysis of the current state-of-the-art on knowledge engineering in the wind energy domain is performed, with available tools put into perspective by establishing the main domain actors and their needs, as well as identifying key problematic areas. Finally, recommendations for further development and improvement are provided.

Yuriy Marykovskiy, Thomas Clark, Justin Day, Marcus Wiens, Charles Henderson, Julian Quick, Imad Abdallah, Anna Maria Sempreviva, Jean-Paul Calbimonte, Eleni Chatzi, and Sarah Barber

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on wes-2023-173', Anonymous Referee #1, 16 Jan 2024
    • AC1: 'Reply on RC1', Yuriy Marykovskiy, 23 Jan 2024
  • RC2: 'Comment on wes-2023-173', Anonymous Referee #2, 21 Jan 2024
    • AC2: 'Reply on RC2', Yuriy Marykovskiy, 23 Jan 2024

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on wes-2023-173', Anonymous Referee #1, 16 Jan 2024
    • AC1: 'Reply on RC1', Yuriy Marykovskiy, 23 Jan 2024
  • RC2: 'Comment on wes-2023-173', Anonymous Referee #2, 21 Jan 2024
    • AC2: 'Reply on RC2', Yuriy Marykovskiy, 23 Jan 2024
Yuriy Marykovskiy, Thomas Clark, Justin Day, Marcus Wiens, Charles Henderson, Julian Quick, Imad Abdallah, Anna Maria Sempreviva, Jean-Paul Calbimonte, Eleni Chatzi, and Sarah Barber
Yuriy Marykovskiy, Thomas Clark, Justin Day, Marcus Wiens, Charles Henderson, Julian Quick, Imad Abdallah, Anna Maria Sempreviva, Jean-Paul Calbimonte, Eleni Chatzi, and Sarah Barber

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Short summary
This paper delves into the crucial task of transforming raw data into actionable knowledge, which can be used by advanced artificial intelligence systems, a challenge that spans various domains, industries, and scientific fields amid their digital transformation journey. This article underscores the significance of cross-industry collaboration and learning, drawing insights from sectors leading in digitalisation and provides strategic guidance for further development in this area.
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