The project investigates methods for adapting the planning of industrial production steps in industrial processes to the availability of renewable energy. When changing Austria’s energy supply to 100% renewables (#mission 2030) energy production becomes increasingly volatile. The primary objective and innovation of the project is to enable production plants to plan their manufacturing steps according to a predetermined yet dynamic energy availability in such a way that a given energy profile is maintained by more than 95%. This means that the given energy is not only not exceeded but also used as fully as possible while at the same time ensuring efficient utilization of manufacturing resources. The big challenge is in the handling of the inherent prediction uncertainty of the energy profiles, which increases with increasing planning horizon and production complexity.

Together with the project partners Danube University develops an iterative production planning process to achieve the above goal. Adaptive energy consumption models that are optimized using machine learning techniques increase the planning accuracy, and the inclusion of production-dependent energy storage and recuperation potentials improves the overall efficiency. The concept presented in the project will enable energy-aware production planning for individual production lines, however it will be scalable to allow multiple independent processes to be jointly adapted and optimized for an overarching energy profile.

Within the scope of the project the developed system will also be tested in an industrial laboratory test in the AVL Battery Innovation Center. The results have therefore highly validity for the entire manufacturing industry. A further generalization to other sectors (e.g., food production, logistics) will also be continuously examined.

The project is part of the CELTIC-NEXT project IEoT (Intelligent Edge of Things) where it constitutes a central use case.

**This project is partially co-funded by Klima- und Energiefonds within the programme Energy Research (e!MISSION).


Duration 01/04/2021 - 29/02/2024
Funding FFG

Department for Integrated Sensor Systems

Center for Distributed Systems and Sensor Networks

Principle investigator for the project (University for Continuing Education Krems) Dipl.-Ing. Albert Treytl


Sauter, T.; Treytl, A. (2023). IoT-Enabled Sensors in Automation Systems and Their Security Challenges. IEEE Sensors Letters, vol. 7, no. 12: 1-4

Howind, S.; Sauter, T. (2023). Modeling Energy Consumption of Industrial Processes with Seq2Seq Machine Learning. In: IEEE, proceedings in 2023 IEEE 32nd International Symposium on Industrial Electronics (ISIE): 1-4, IEEE, Helsinki, Finnland

Bratukhin, A.; Franzl, G.; Karameti, D.; Treytl, A.; Sauter, T. (2022). Probability-based, Risk-adjusted Energy Consumption Optimisation in Industrial Applications. In: IEEE, Proceedings in 2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA): 1-8, IEEE, Stuttgart

Bratukhin, A.; Treytl, A.; Howind, S.; Estaji, A.; Sauter, T. (2021). Integrating uncertainty of available energy in manufacturing planning. In: IEEE, Proceedings in 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA ), 2021: 1-4, IEEE, Vasteras, Schweden


Probability-based, Risk-adjusted Energy Consumption Optimisation in Industrial Applications

ETFA 2022, 07/09/2022


project partners

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