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Self-Organized Study Groups

Die Graduierten arbeiten in Self-Organized Study Groups (SOSGs), die jeweils aus 3-5 Mitgliedern bestehen, gemeinsam an Projekten zu speziellen Fragestellungen aus dem Kontext Energiezustandsdaten. Auf diesen Seiten möchten wir die Study Groups kurz vorstellen.


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

The increasing share of intermittent energy generation in the electricity system comes with great challenges. Demand side management is frequently mentioned as one measure to tackle those challenges. Flexibility on the demand side is essential for the success of these measures. Extensive research exists that describes, models and optimises 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 mathematically describe demand side flexibility in smart grids integrating various 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)

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

Energy is an essential part of modern life and requires constant supervision to maintain a stable supply. Tracking energy consumption for billing, forecasting and similar analytic purposes is often performed in time intervals of 15 minutes. From a technical point of view, measuring energy consumption is possible with a sample rate of seconds. The benefit of collecting high-resolution smart meter data for subsequent analysis purposes is not well understood. The expected loss of information introduced by reducing the sample rate or by using aggregation methods depends on the data 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 analytical results.

The focus of our investigation is the production facility of the Institute of Power Electronics (IPE) at KIT Campus North. In this factory, each of the industrial processing machines is instrumented with a high-resolution smart meter. To make 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 to energy storage systems to run the production on renewable energy sources. 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.

Apart from this primary research on the high-resolution energy data, we also target a publication of the data in a privacy compliant form. Because most other public data sets on energy come from households, this data will allow researchers to work on data analysis challenges in an industrial context.