Data-based digital predictive methods are becoming increasingly important throughout industry. Especially in the energy industry, predictive modelling is of utmost importance, as it enables more effective decisions and predictions to be made on the basis of existing data. However, because models can only be as good as their data, our Visual Analytics research group is looking for methods to validate large amounts of data to ensure that analysis is as error-free as possible.
Focusing on real application needs of our partners (APG, AVL List, HAKOM Time Series GmbH and Plasmo Industrietechnik) from the automotive, industrial and energy sectors, we developed novel Visual Analytics methods for more efficient visualization of large, high-dimensional data in the TOHIVA research project. The optimization tools for existing software that resulted from this project enable predictive models that provide solutions for specific tasks, e.g. for quality assessment of data or multi-criteria decision support.
The visual computing solution TSM Visuals, developed in cooperation with our company partner HAKOM Time Series GmbH and data scientists from the energy industry, provides completely new possibilities to explore, interpret and evaluate large and heterogeneous data by simultaneous interactive visualizations. This creates the basis for valid analysis results by quality assessment of the data and serves, among other things, for quality assurance of complex procedures.
Renewable energies, such as hydropower, are an essential backbone for a climate-friendly future. To enable better real-time monitoring and thus maintenance of hydropower infrastructure, innovative digital hydropower twins are being developed in this project.
The goal of the applied research project En2VA (“Visual Analytics for Energy and Engineering Applications”) is to increase the efficiency and the quality of advanced analytics for high-dimensional data from manufacturing, engineering, and the energy sector.