open access publication

Preprint, 2024

Knowledge Engineering for Wind Energy

EGUsphere, Volume 2024, Pages 1-51, 10.5194/wes-2023-173

Contributors

Marykovskiy, Yuriy 0000-0002-6570-6966 [1] [2] Clark, Thomas [3] Day, Justin [4] Wiens, Marcus 0000-0002-1646-1691 [5] Henderson, Charles [6] Quick, Julian [7] Abdallah, Imad 0000-0001-8678-0965 [2] Sempreviva, Anna Maria 0000-0003-4124-9040 [7] Calbimonte, Jean-Paul 0000-0002-0364-6945 [8] Chatzi, Eleni N 0000-0002-6870-240X [2] Barber, Sarah 0000-0002-7329-6434 [1]

Affiliations

  1. [1] Ostschweizer Fachhochschule OST
  2. [NORA names: Switzerland; Europe, Non-EU; OECD];
  3. [2] ETH Zurich
  4. [NORA names: Switzerland; Europe, Non-EU; OECD];
  5. [3] British Antarctic Survey
  6. [NORA names: United Kingdom; Europe, Non-EU; OECD];
  7. [4] Pacific Northwest National Laboratory
  8. [NORA names: United States; America, North; OECD];
  9. [5] Fraunhofer Institute for Wind Energy Systems
  10. [NORA names: Germany; Europe, EU; OECD];

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.

Keywords

actors, analysis, area, area of knowledge engineering, article, artificial intelligence systems, concept, data, development, digital transformation, domain, domain actors, domain experts, domain knowledge, energy, energy domain, energy sector, engineering, evolution, experts, improvement, intelligent systems, knowledge, knowledge engineering, knowledge representation, knowledge-base, knowledge-based systems, next‐generation artificial intelligent system, perspective, problematic areas, recommendations, representation, sector, source, source of knowledge, system, systematic analysis, tools, transformation, wind, wind energy, wind energy domain, wind energy sector

Funders

  • Swiss National Science Foundation

Data Provider: Digital Science