Ontologies for wind energy domain experts – Recommended Practice

Recently recognised as a "megatrend" in the wind energy sector [1], digitalisation encompasses the organisational and industry-wide adoption of data and digital technologies to enhance efficiency, generate insights, and innovate products and services [2]. A major obstacle to advancing digitalisation in the wind energy sector is the absence of widely adopted, community-driven information models [2, 3, 4]. This hinders seamless information exchange and knowledge management [4]. In particular, improving interoperability and data sharing among stakeholders is required to support advancements in technologies such as Intelligent Digital Twins and Artificial Intelligence (AI) [5].  Ontologies make data AI-ready by providing a structured, shared vocabulary that defines relationships and semantics, enabling machines to interpret, integrate, and reason over information consistently. The word “ontology” is defined as an explicit specification of a shared conceptualisation [6], meaning an abstract model of the world that identifies entities, their properties, and relationships. The concepts defined in an ontology are specified not only with clear syntax but also explicit semantics, thus reducing uncertainty for their interpretation. In digitalising the wind energy sector, ontologies enable precise formalisation of knowledge—from defining blade anomaly terms or inference rules for Failure Modes Effects and criticality analysis to modelling the entire domain. Given the complexity and evolution of information in the sector, ontologies must be modular, extensible, and interoperable. Following ontology engineering best practices ensures knowledge and data remain findable, accessible, interoperable, and reusable (FAIR) [7]. In this work, we present the new IEA Wind Recommended Practice “Ontology creation, publication, and maintenance for wind energy domain experts” [8], and show how it is being applied using a new ontology publishing tool, the TechnoPortal [9], within the WeDoWind Information Modelling Community [10] to create and share ontologies, in order to increase the FAIRness and AI-readiness of wind energy data.