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Self-Organized Study Groups

The members of the research training group and the associates have teamed up in so-called Self-Organized Study Groups (SOSGs), consisting of three to five members. Each study group is working on a project with specific research questions related to energy status data. The current groups are as follows.


Members: Lukas Barth (ITI), Nicole Ludwig (IAI), Esther Mengelkamp (IISM), Philipp Staudt (IISM)

The increasing share of intermittent energy generation is a big challenge for the electricity system. Demand side management has been touted as one measure to tackle this challenge. Flexibility on the demand side is essential for its success. Extensive research exists that describes, models and optimizes various processes with flexible electrical demands. However, most of these approaches are very process-specific, and there is no unified notation. In this Self-organized Study Group, we develop a comprehensive modelling framework to formally describe demand side flexibility in smart grids, integrating various kinds of constraints from different existing models. We aim at providing a universally applicable modelling framework for demand side flexibility.

M.E.G.A. (Manufacturing Energy Data – Generation and Analysis)

Members: Simon Bischof (IPE), Holger Trittenbach (IPD), Michael Vollmer (IPD), Dominik Werle (IPD)

Maintaining a stable supply of energy requires constant monitoring. Tracking energy consumption for billing, forecasting and other analytic purposes often takes place in time intervals of 15 minutes. From a technical point of view however, measuring energy consumption is possible with a sample rate of seconds. But the benefits of collecting such high-resolution smart meter data for subsequent analysis purposes currently are not well understood. The expected loss of information introduced by reducing the sample rate or by relying on aggregated data depends on the specific analytics task performed. The research question of this self-organized study group is to identify and quantify the impact of aggregation on the quality of data-analysis results.

The object of our investigation is the production facility of the Institute of Power Electronics (IPE) at KIT Campus North. In this factory, each industrial processing machine is instrumented with a high-resolution smart meter. To realize the trade-off between data resolution and data-analysis quality, we compare the raw sensor data and aggregated values on different analytics tasks such as data stream clustering, classification and correlation analysis. Another use case is to identify the requirements on energy storage systems to run the production on renewable energy. It is important to identify the level of aggregation needed to perform such an analysis, because the requirements derived from the data might change with the level of aggregation.

In addition to this research, we envision publishing the data, and we plan to do in a privacy-preserving manner. Because many other open data sets in the energy domain come from private households, this data will allow researchers to work on data analysis challenges in an industrial context.