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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, Sarah Barber

TL;DR

This paper addresses how knowledge engineering and knowledge representation can unlock value from the vast wind-energy data landscape. It surveys the KE scope, conceptual KR foundations, and practical technologies (notably Semantic Web, RDF, and OWL) that enable machine-interpretable domain knowledge and digital twins. It then assesses the wind-energy artefact landscape, revealing gaps in adoption, standardisation, and cross-domain alignment, and provides guidelines to foster a more open, interoperable, and resilient knowledge ecosystem. The work highlights the potential of ontology-based data integration and shared semantic artefacts to drive improved diagnostics, predictive maintenance, and decision support in wind-power systems. Overall, it offers a coherent framework and actionable recommendations to accelerate the digital transformation of the wind energy sector through knowledge engineering.

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 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.

Knowledge Engineering for Wind Energy

TL;DR

This paper addresses how knowledge engineering and knowledge representation can unlock value from the vast wind-energy data landscape. It surveys the KE scope, conceptual KR foundations, and practical technologies (notably Semantic Web, RDF, and OWL) that enable machine-interpretable domain knowledge and digital twins. It then assesses the wind-energy artefact landscape, revealing gaps in adoption, standardisation, and cross-domain alignment, and provides guidelines to foster a more open, interoperable, and resilient knowledge ecosystem. The work highlights the potential of ontology-based data integration and shared semantic artefacts to drive improved diagnostics, predictive maintenance, and decision support in wind-power systems. Overall, it offers a coherent framework and actionable recommendations to accelerate the digital transformation of the wind energy sector through knowledge engineering.

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 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.
Paper Structure (49 sections, 6 equations, 6 figures, 7 tables)

This paper contains 49 sections, 6 equations, 6 figures, 7 tables.

Table of Contents

  1. Introduction
  2. Extracting value from data
  3. The need for managing data
  4. The challenge addressed in this paper
  5. Contribution of this paper
  6. Knowledge engineering: scope and activities
  7. Knowledge representation: conceptual overview
  8. Understanding representation: semantics, pragmatics, context, and metadata
  9. Expressing representation: language
  10. Interpreting representation: logic
  11. Representation assumptions: open world vs. closed world
  12. Formalising representations: ontology
  13. Common representation: standard
  14. Knowledge representation: technologies
  15. Semantic Web
  16. ...and 34 more sections

Figures (6)

  • Figure 1: Typical roles and activities within the digitalisation process
  • Figure 2: UML diagram representing a part of schema.org ontology as a labelled graph.
  • Figure 3: Semantic web stack Nowack2009
  • Figure 4: Analysis of semantic artefacts adoption levels. Low adoption levels in wind energy domain can be attributed to low availability and lack of active development.
  • Figure 5: Semantic artefacts adoption in digital twins and decision support systems based on literature review
  • ...and 1 more figures