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Forschung etalytics

EISKIG: Research with practical relevance

Smart Energy Management System for
Industrial Cooling Solutions

Energy Intelligence in Industrial Buildings

EISKIG optimises industrial cooling systems in practice

The EISKIG research project uses data– and AI–based optimization methods in building refrigeration technology to identify corresponding energy wastes or efficiency potentials, derives measures for optimized plant operation and implements them in industrial practice at the project partners. The project is led in charge by etalytics and the PTW Institute at TU Darmstadt.
 

The goal is to achieve at least 15% energy savings in selected cooling supply systems.

TP 1: System Understanding and Conception

  • Collection of technical and organizational requirements of the application partners and
  • Definition of system boundaries

TP 2: Dateninfrastruktur

  • Connection of the real systems of the application partners via IoT gateways
  • Establishment of a central data processing platform for forecasting, simulation and optimization applications

TP 3: Data Infrastructure

  • Analysis of the acquired time series data
  • Application of statistical methods and machine learning methods
  • Goal: To gain deeper insights into the dependence of the energy system on external influences and their prognosis

TP 4: Digital twins

  • Simulative mapping of the selected energy systems based on the recorded data
  • Testing of the optimization of plant operation on simulation models

TP 5: Operational optimization

  • Optimization of real plant operation using the findings from SP 3 (Data Analysis) and SP 4 (Digital Twins)

TP 6: Scaling

  • Development of software modules to simplify the application and transfer of optimization processes to other companies

Energy Intelligence in Industrial Buildings

Shared Vision: AI-Driven Optimization of Industrial Cooling Systems

All partners share the common objective of lowering energy costs and CO₂ emissions. EISKIG aims to develop a self-operating system that leverages AI-based optimization techniques to independently analyze and enhance the operational strategy of industrial cooling systems. The system is intended to boost energy efficiency and flexibility, reduce implementation complexity, and promote greater user acceptance.

Overview and objectives of the project

The images used in this video were (partially) generated with the help of artificial intelligence (e.g., Midjourney, DALL·E) and do not reflect real events.

Video from PTW, TU Darmstadt © 2025

Methods used in the project

The images used in this video were (partially) generated with the help of artificial intelligence (e.g., Midjourney, DALL·E) and do not reflect real events.

Video from PTW, TU Darmstadt © 2025

From practice to research

Consortium partners

Project Management by Jülich
and supported by the Federal Government

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