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Energy Management & Monitoring

Digitalization in the industry is advancing – the data quantity from production control & planning, process monitoring, energy meters and numerous other sensors has been growing exponentially since the beginning of the computer age. Energy data represents an essential basis for the identification and evaluation of efficiency potentials. In addition, control technology measures can raise significant potential for increasing energy efficiency and energy flexibility. The focus of our research is on answering the following questions, which are of high relevance for the industry:

1 | Data Acquisition

What measurements need to be taken in relation to previously defined objectives in energy management (measuring point concepts for energy & operating data)?

How can low-cost sensors, mobile measurement concepts, simulations or „smart“ disaggregation algorithms reduce the investment costs for measuring technology?

What ICT systems can be used to capture and compress large amounts of data in real time and high resolution?

Which further data (e.g. production and machine data) should be recorded together with energy data to show possible correlations?

2 | Energy Management & Monitoring

What do intelligent energy management systems look like?

Which key performance indicators are needed and how are they formed?

How can a system support its user with that task?

How can performance indicators and measured values be presented in line with the target group?

3 | Condition Monitoring and Predictive Maintenance

How does the recorded data correlate with the physical states of machines, components and the part quality?

How can complex coherences be revealed, e.g. through machine learning?



  • ICT Architectures (Interfaces and Protocols)

  • Measuring Point Concepts for Energy and Operational Data

  • Real-Time Data Acquisition & Data Streaming Analytics

  • Low-Cost-Sensors and Disaggregation

  • Embedded Systems / Smart Monitoring

  • Guided Energy Monitoring

  • Condition Monitoring and Predictive Maintenance