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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2310.00804 |
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| _version_ | 1866910408765341696 |
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| author | Marykovskiy, Yuriy Clark, Thomas Day, Justin Wiens, Marcus Henderson, Charles Quick, Julian Abdallah, Imad Sempreviva, Anna Maria Calbimonte, Jean-Paul Chatzi, Eleni Barber, Sarah |
| author_facet | Marykovskiy, Yuriy Clark, Thomas Day, Justin Wiens, Marcus Henderson, Charles Quick, Julian Abdallah, Imad Sempreviva, Anna Maria Calbimonte, Jean-Paul Chatzi, Eleni Barber, Sarah |
| contents | 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 artificially intelligent systems. To this end, this article highlights the role that knowledge engineering can play in the process of 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 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 and identifying key problematic areas. Finally, guidelines for further development and improvement are provided. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_00804 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Knowledge Engineering for Wind Energy Marykovskiy, Yuriy Clark, Thomas Day, Justin Wiens, Marcus Henderson, Charles Quick, Julian Abdallah, Imad Sempreviva, Anna Maria Calbimonte, Jean-Paul Chatzi, Eleni Barber, Sarah Artificial Intelligence 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 artificially intelligent systems. To this end, this article highlights the role that knowledge engineering can play in the process of 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 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 and identifying key problematic areas. Finally, guidelines for further development and improvement are provided. |
| title | Knowledge Engineering for Wind Energy |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2310.00804 |