In the DIH 2.0 project, the LUT Business School (LBM) is responsible for communicating with the project's target groups (local businesses) to identify their needs and how the LAB and LUT's capabilities can be brought into the DIH service model and its functionalities. In addition, the School of Energy Systems (LES) at LUT is responsible for piloting the proof-of-concept DIH functionality by looking at how to build a concrete data-driven DIH service model through a case study. The case study is intended to address the knowledge needs of South Karelia companies in relation to the use of intelligent algorithms as part of technology products. The case study of the LUT project is linked to the energy revolution which is strongly interconnected with digitalization. For example, often integrating a new technology into an existing energy system requires the use of data-driven methods. The project case study therefore aims to contribute to the adoption of energy technologies and to deliver a DIH service model through which, for example, the digital problems basing on artificial intelligence (AI) of energy companies can be solved more efficiently.



LUT's LBM project aims to organise interviews with local associations to identify service models for LUT and LAB as part of the DIH functionality. The interviews will play an important role in identifying the suitability of the LES-led sub-set and its service model for the needs of enterprises. This interview process will be replicated to support other LUT and LAB units' capabilities that bring useful features to local businesses. The LUT LES-led sub-project aims to bring important service functionality into the DIH ecosystem, where the driving theme is the integration of artificial intelligence (AI)-based methods into the digital platform.  The aim is to commercialise the service models of the innovation hub, of which the AI-Hub, focusing on data and its exploitation, would be one concrete project outcome. In this DIH2.0 sub-project, LES will develop data-driven analytical tools that can be generalised to industrial needs, allowing data collected from different processes to be modelled in a straightforward way and the added value of the data to be exploited to build intelligent system ensembles (control and diagnostics). More specifically, the sub-project aims to build a service model for data analytics to be integrated into DIH, which will serve as a proof-of-concept implementation of the main project to evaluate potential service models for DIH, helping DIH-linked companies to implement their digital solutions.


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