OpenMeter – Data and Analysis Platform to Increase Energy Efficiency

The digitalization of the energy industry means that more and more measurement data is being recorded. The demand for measurement data from the energy system is also increasing. While there is a good public data situation for energy production as a result of transparency obligations, at least for larger production units, there is a lack of publicly accessible, real measurement data from energy consumers. Such consumption data, however, are the necessary basis for essential innovations in the course of the energy turnaround, for example for energy efficiency improvement and evaluation, for intelligent network planning for smart grids, and for the interdisciplinary development of innovative services and business models based on artificial intelligence and big data analytics.

It is therefore a concern of research as well as industry and public institutions to obtain simple, open access to diverse and extensive real consumption data from the German energy system.

© BMWi

Digital Open Data Platform »Open Energy Meter Data«

The aim of the project is the development and establishment of a digital open data platform »Open Energy Meter Data« for energy consumption data as well as the implementation of transdisciplinary data-based use cases with the participation of the disciplines electrical engineering, technomathematics and computer science. The benefit of a broad, open database in the context of the digitization of the German energy system is to be demonstrated and such a database implemented. Based on a high-performance open data platform, mathematical methods of artificial intelligence will be combined with expert knowledge from the field of energy technology.

The open data platform will be complemented by a prototypical web-based analysis platform, which will contribute to the sustainable provision and growth of the open data platform.  

The Goals of the Fraunhofer ITWM at a Glance:

  • Research and evaluation of data-based machine learning methods in energy consumption profiles to enable a para-metric prognosis of energy consumption.
  • Development and validation of models for the prognosis of energetic baselines based on temporal consumption data.
  • Development and validation of models for the prognosis of the effects of energetic actions on the basis of temporal consumption data.
  • Identification of use cases that can be realized with machine learning methods on energy consumption data.

Project partners

  •  Institute of Energy Systems, Energy Efficiency and Energy Economics (ie3), TU Dortmund
  • logarithmo GmbH & Co.KG
  • Discovergy GmbH
  • City of Wuppertal
  • Energieagentur Rheinland-Pfalz GmbH