Looking for the Swiss Wind Energy R&D Network? We’ve moved to www.windenergynetwork.ch
Looking for the Swiss Wind Energy R&D Network? We’ve moved to www.windenergynetwork.ch
Looking for the Swiss Wind Energy R&D Network? We’ve moved to www.windenergynetwork.ch
Looking for the Swiss Wind Energy R&D Network? We’ve moved to www.windenergynetwork.ch


Data scientists can spend up to 80% of their time on data cleaning and preparation, leaving only 20% for value-creating activities such as data science, data interpretation and the development of new tools, apps and business models. We want to help you reverse this trend! Imagine how much more efficient your organisation would be if your data scientists spent 80% of their time with value-creating activities. From 20% to 80% is an increase of a factor of four!
Part of this can be achieved by using agreed-upon and aligned data models and ontologies. We can help!

Do you want to know what the state-of-the art data models and ontologies are, and how you can use them?

We can advise you on topics related to knowledge engineering, including ontology, taxonomy and vocabulary development, as well as FAIR data and data standards. Examples of possible questions you may have include:
  • Why should I align my data models with other data models?
  • How should I align my data models with other data models?
  • How can I best structure my data and metadata to be able to become more efficient in data sharing?
  • What is a data model? What is an ontology?
  • What are knowledge-based systems?
  • Why are ontologies important for knowledge-based systems?
  • How can I most effectively use AI-based systems in my organisation?
Our consulting is based upon the knowledge we gained in the creation of the recent review paper "Knowledge engineering for wind energy". This paper 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.
As well as this, we are involved in several efforts aiming to improve data sharing in the wind energy sector, including IEA Wind Task 43, RDA, the WindEurope Digitalisation Task Force and the European Academy of Wind Energy Digitalisation Committee.

Do you want to publish open data sets but have no time or resources to prepare them?

If you have some data sets you wouldn't mind sharing with the wider community but simply don't have the time or resources to prepare them, then we can help! This can be any type of data, including measurements from a research wind turbine, wind tunnel or laboratory data, operating wind turbine data, or even simulation data! It could also include different types of confidentiality restrictions and license types.
The different data preparation steps include:
  • Defining your confidentiality and license requirements.
  • Transferring the data to our local server.
  • Collecting unstructured or structured metadata from you (i.e. descriptions of the data sets and the relevant conditions in any format that is currently available).
  • Ingesting and cleaning the data into our data preparation pipeline.
  • Organising the data in a sensible and understandable way, depending on the planned usage.
  • Assessing the data according to the FAIR data maturity model, in which different scores can be assigned to various indictors in the categories of "Findable", "Accessible", "Interoperable" and "Reusable".
  • Carrying out improvements to the data and metadata based on the FAIR data maturity model in order to increase the scores. Examples of things that could be done here include:
    • Uploading the data to a recognised FAIR data repository such as Zenodo or the Bridge of Knowledge.
    • Creating a draft schema to describe the sensor specifications and placement, encoded as JSON and YAML schemas.
    • Following and slightly modifying existing ontologies such as WindIO.
    • Aligning and integrating data and metadata semantic artefacts with existing community projects including IEA Wind Task 43, TIM Wind and the WindEurope Digitalisation Task Force.
  • Publishing the data and metadata at the required location according to the required licenses or confidentiality rules.
  • Optional: making sure the community sees and uses your data by creating and running WeDoWind challenges together.

Contact us