IEA IETS Task 18: Digitalization, Artificial Intelligence and Related Technologies for Energy Efficiency and GHG Emissions Reduction in Industry (Working period 2023 - 2024)

The work in Task 18 enables the exchange of experience and knowledge between industry and research institutions from different countries. Through this cooperation best practices are identified and disseminated to promote the implementation of energy-efficient technologies in industry. In the medium and long-term, this contributes to reduce energy consumption and greenhouse gas emissions of industry.

Short Description

Objectives

The international IEA IETS Task 18 pursues the overarching goal of advancing the knowledge, development and application of digitalization, AI and related technologies to improve the economic and environmental performance of energy and GHG-intensive industries.

As the research and development environment in the field of AI and digital technologies is extremely dynamic and there are often concerns about confidentiality, an open structure was chosen for Task 18. Participating organizations themselves specify which projects and results they are willing to share in the task. This lowers the hurdle for participation and is intended to facilitate the exchange of knowledge and cooperation. The aim of Task 18 is to make the information accessible and to critically reflect on it with experts from the participating organizations.

The following key activities are planned:

Knowledge sharing on effective AI approaches for industrial energy systems and their evaluation by experts from the participating organizations. To this end, there will again be a series of presentations on (best) practice examples, which will be made publicly available on YouTube.
Development of metrics for an appropriate assessment of the costs and benefits of AI applications in industrial energy systems. The aim is to quantify the costs and benefits (monetary and non-monetary) of AI applications. We plan to apply the developed methodology to several reference projects.
Development of a technology roadmap for the targeted further development of AI approaches for use in industrial energy systems, considering the specific needs of industrial companies and the capacities of participating research institutions.

Prof. Hofmann (TU Wien) is the subtask-lead of the Subtask "Methods and applications of digital twins" and will also be the subtask-lead of the Subtask "KI methods for industrial energy systems". The members of the Austrian will be in charge of planning, organizing and documenting the joint activities within these subtasks.

Expected results

The unique selling point of Task 18 is the link between digitalization and AI methods and improving energy efficiency as well as reducing greenhouse gas emissions in industry.

The following results should be available at the end of the project

Compilation of innovative AI approaches for industrial energy systems and potential applications for decision support.
Innovative metrics for a comparable, standardized evaluation of the effort and benefits of AI applications in industrial energy systems. This should reduce the lack of clarity with regard to the costs/benefits of AI applications.
Technology roadmap for the targeted further development of AI approaches.

Participants

Austria, Canada, Denmark, Finland, France, Germany, Portugal, Netherlands, Italy, Sweden, Switzerland

Contact Address

Project leader

Technische Universität Wien
Institute of Energy Systems and Termodynamics
Univ.-Prof. Dr. René Hofmann
Getreidemarkt 9/E302, 1060 Wien
E-Mail: rene.hofmann@tuwien.ac.at
Tel: +43 1 58801 302327

Project partners

AIT Austrian Institute of Technology GmbH (AIT)
Center for Energy – Sustainable Thermal Energy Systems
DI Sophie Knöttner
Giefinggasse 2, 1210 Wien
E-Mail: Sophie.knoettner@ait.ac.at
Tel: +43 664 88 90 43 37

AEE – Institute for Sustainable Technologies (AEE INTEC)
Jürgen Fluch
Feldgasse 19, 8200 Gleisdorf
E-Mail: j.fluch@aee.at
Tel: +43 (0) 3112 5886 454

Montanuniversität Leoben
Chair of Energy Network Technology
Univ.-Prof. Dr. Thomas Kienberger
Franz-Josef-Str. 18, 8700 Leoben
E-Mail: thomas.kienberger@unileoben.ac.at
Tel: +43 3842 402 5400