WESgraph: a graph database for the wind farm domain

Quaeghebeur, Erik; Perez-Moreno, Sebastian Sanchez; Zaaijer, Michiel B.

doi:10.5194/wes-5-259-2020

Cited by 5 publications

(5 citation statements)

References 18 publications

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“…Top-level ontology and a knowledge base for the wind farm domain, implemented as a graph database by Quaeghebeur et al (2020).…”

Section: Wesgraphmentioning

confidence: 99%

Knowledge engineering for wind energy

Marykovskiy,

Clark,

Day

et al. 2024

Wind Energ. Sci.

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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 and from other sectors. This article addresses the challenges faced by wind energy domain experts in converting data into domain knowledge, connecting and integrating them with other sources of knowledge, and making them 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.

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“…Top-level ontology and a knowledge base for the wind farm domain, implemented as a graph database by Quaeghebeur et al (2020).…”

Section: Wesgraphmentioning

confidence: 99%

Knowledge engineering for wind energy

Marykovskiy,

Clark,

Day

et al. 2024

Wind Energ. Sci.

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“…However, this is required when performing incorporated analysis or modeling. To assist researchers keep this outline, the Wind Energy System graph(WES graph) is made by Erik et al [25], , a knowledge base for the wind farm domain, implemented as a graph database. It at present contains 1222 concepts and 1725 relations between them.…”

Section: Researches Based On Graph Databasementioning

confidence: 99%

NO SQL Database: Graph database

Elsabagh

2022

Egyptian Journal of Artificial Intelligence

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The database of NoSQL is considered one the most significant technology in the current era of computer science especially, with the emergency of big data. The issue of processing and storing data is solved by utilizing the NoSQL databases. Planning to offer references to the users of No SQL databases, this survey examines the characterization, categories, and hypothetical premise of NoSQL dependent on the introduction of the rise, improvement, and development of relational database to NoSQL and the examination of its restrictions of relational databases in the current era. Also, this survey points to a type of NoSQL database called graph database. It can be characterized as those in which the architecture for instances and schema are demonstrated as graphs and data control is described by graph oriented processes and activities and type constructors. It started in eighties and nineties close by object arranged models. Their impact slowly ceased to exist with the rise of other models, specifically XML, spatial, and semi-structured. Lately, the requirement to manipulate information with graph like nature has restored the relationship of this field. The fundamental goal of this review is to introduce the work that has been proposed in the field of graph database.

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“…While few existing studies [36]- [38] have demonstrated the promise of utilising KGs for systematically structuring conceptual information in the wind industry, they only serve as an end point [39] providing information for consultation without the ability to further reason over the data. Moreover, these ontologies need to be queried manually through specialised graph query languages to extract relevant and meaningful information, which may not be easily accessible to turbine engineers & technicians.…”

Section: Introductionmentioning

confidence: 99%

Automated Question-Answering for Interactive Decision Support in Operations & Maintenance of Wind Turbines

Chatterjee

Dethlefs

2022

IEEE Access

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Intelligent question-answering (QA) systems have witnessed increased interest in recent years, particularly in their ability to facilitate information access, data interpretation or decision support. The wind energy sector is one of the most promising sources of renewable energy, yet turbines regularly suffer from failures and operational inconsistencies, leading to downtimes and significant maintenance costs. Addressing these issues requires rapid interpretation of complex and dynamic data patterns under time-critical conditions. In this article, we present a novel approach that leverages interactive, natural language-based decision support for operations & maintenance (O&M) of wind turbines. The proposed interactive QA system allows engineers to pose domain-specific questions in natural language, and provides answers (in natural language) based on the automated retrieval of information on turbine subcomponents, their properties and interactions, from a bespoke domain-specific knowledge graph. As data for specific faults is often sparse, we propose the use of paraphrase generation as a way to augment the existing dataset. Our QA system leverages encoder-decoder models to generate Cypher queries to obtain domain-specific facts from the KG database in response to user-posed natural language questions. Experiments with an attention-based sequence-to-sequence (Seq2Seq) model and a transformer show that the transformer accurately predicts up to 89.75% of responses to input questions, outperforming the Seq2Seq model marginally by 0.76%, though being 9.46 times more computationally efficient. The proposed QA system can help support engineers and technicians during O&M to reduce turbine downtime and operational costs, thus improving the reliability of wind energy as a source of renewable energy.

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WESgraph: a graph database for the wind farm domain

Cited by 5 publications

References 18 publications

Knowledge engineering for wind energy

Knowledge engineering for wind energy

NO SQL Database: Graph database

Automated Question-Answering for Interactive Decision Support in Operations & Maintenance of Wind Turbines

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