Data analysis for improved energy efficiency

Energy systems that are to ensure power supply in the future produce large amounts of data. Young scientists working in the new research training group ‘Energiezustandsdaten – Informatik-Methoden zur Erfassung, Analyse und Nutzung’ (energy data – computerised methods for their acquisition, analysis and use), which is funded by The German Research Foundation (DFG), will apply this data to understand processes taking place in the different components of energy supply systems.

For example, ageing of batteries may probably be understood better by analysing appropriate measurement data. On this basis, scientists hope to optimise existing systems and, thus, to contribute to a more efficient energy supply. “A major prerequisite for reducing energy consumption is to determine it precisely. Thanks to the digitisation of energy systems, this is possible. Now, we have to find ways to study the data volumes such that potential problems are identified and solved and energy systems are improved,” the spokesperson of the research training group, Klemens Böhm, Professor at the KIT Institute for Program Structures and Data Organization (IPD), explained.

Research to be conducted under the new research training group is aimed at analysing data to find weaknesses of the energy system and to make the latter more flexible, more efficient and more fail-safe. “But we should not lose sight of user friendliness. When analysing personal consumption data, it is important to secure privacy,” Böhm commented. To achieve the ambitious objectives, the research training group pursues an interdisciplinary approach. Ten KIT institutes of various disciplines are involved. The consortium of scientists includes experts in the area of big data as well as scientists specialised in IT security, information legislation and electrical engineering.

For many years now, research into innovative energy concepts has been conducted under various projects of KIT. Thanks to interdisciplinary cooperation, the researchers involved in the new research training group will profit from the excellent energy research infrastructure of KIT. Findings from large scale research projects can be incorporated directly in their work. An example is the ‘EnergyLab 2.0’, a platform to test innovative energy concepts. When operating this platform, real data will be produced that may then be used by the researchers for analysis. Methods developed by computer scientists can be tested under real conditions. Vice-versa, the evaluation results can be considered in the EnergyLab 2.0 project.