Auflistung nach Autor:in "Kramer, Oliver"
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- KonferenzbeitragAn Evolutionary Approach to Geo-Planning of Renewable Energies(Proceedings of the 28th Conference on Environmental Informatics - Informatics for Environmental Protection, Sustainable Development and Risk Management, 2014) Lückehe, Daniel; Kramer, Oliver; Weisensee, ManfredRenewable energy sources are getting more and more important in many industrial nations. As their behavior and effectiveness often depends on their location, the employment of geo-planning and geo-optimization strategies improves their value. A geo-planning process must consider multiple aspects and different requirements resulting in a constrained optimization problem. In this work, we introduce a new optimization approach for geo-planning based on evolutionary strategies. For this sake, we adapt evolutionary operators and employ deterministic parameter control. We define an experimental setting for wind turbines with different potentials, constraints, and wake effects. In the experimental part of this work, we first show the behavior of the approach on toy settings. Extended settings with real-world geographical data, ground mounted solar power plants, and political conditions demonstrate the flexibility and extensibility of the approach.
- ZeitschriftenartikelEnergieinformatik(Wirtschaftsinformatik: Vol. 56, No. 1, 2014) Goebel, Christoph; Jacobsen, Hans-Arno; Razo, Victor; Doblander, Christoph; Rivera, Jose; Ilg, Jens; Flath, Christoph; Schmeck, Hartmut; Weinhardt, Christof; Pathmaperuma, Daniel; Appelrath, Hans-Jürgen; Sonnenschein, Michael; Lehnhoff, Sebastian; Kramer, Oliver; Staake, Thorsten; Fleisch, Elgar; Neumann, Dirk; Strüker, Jens; Erek, Koray; Zarnekow, Rüdiger; Ziekow, Holger; Lässig, JörgAufgrund der zunehmenden Bedeutung einer nachhaltigen Energieerzeugung und eines sparsameren Verbrauchs hat sich die Energieinformatik (EI) zu einem florierenden Forschungsgebiet innerhalb der (Wirtschafts-)Informatik entwickelt. Der Beitrag versucht, dieses neue und dynamische Forschungsfeld durch die Beschreibung aktueller Themen und Methoden der Energieinformatikforschung zu charakterisieren, und gibt einen Ausblick auf die mögliche zukünftige Entwicklung. Zwei generelle Forschungsfragen haben bislang die meiste Aufmerksamkeit auf sich gezogen und werden die EI-Forschungsagenda wahrscheinlich auch in den nächsten Jahre dominieren: Wie kann Informations- und Kommunikationstechnologie (IKT) dabei helfen, (1) die Energieeffizienz zu erhöhen und (2) dezentrale erneuerbare Energiequellen in das Stromnetz zu integrieren. Die Autoren stellen ausgewählte Forschungsarbeiten aus dem EI-Bereich vor und zeigen, wie diese Forschungsfragen in konkrete Forschungsprojekte münden und wie EI-Forscher Beiträge auf der Grundlage ihres jeweiligen akademischen Hintergrundes erbracht haben.AbstractDue to the increasing importance of producing and consuming energy more sustainably, Energy Informatics (EI) has evolved into a thriving research area within the CS/IS community. The article attempts to characterize this young and highly dynamic field of research by describing current EI research topics and methods and provides an outlook of how the field might evolve in the future. It is shown that two general research questions have received the most attention so far and are likely to dominate the EI research agenda in the coming years: How to leverage information and communication technology (ICT) to (1) improve energy efficiency, and (2) to integrate decentralized renewable energy sources into the power grid. Selected EI streams are reviewed, highlighting how the respective research questions are broken down into specific research projects and how EI researchers have made contributions based on their individual academic background.
- KonferenzbeitragSupport Vector Machines for Wind Energy Prediction in Smart Grids(Proceedings of the 27th Conference on Environmental Informatics - Informatics for Environmental Protection, Sustainable Development and Risk Management, 2013) Kramer, Oliver; Treiber, Nils André; Gieseke, FabianIn recent years, there has been a significant increase in energy produced by sustainable resources like wind- and solar power plants. This led to a shift from traditional energy systems to so-called smart grids (i.e., distributed systems of energy suppliers and consumers). While the sustainable energy resources are very appealing from an environmental point of view, their volatileness renders the integration into the overall energy system difficult. For this reason, shortterm wind and solar energy prediction systems are essential for balance authorities to schedule spinning reserves and reserve energy. In this chapter, we build upon our previous work and provide a detailed practical analysis of several wind energy learning scenarios. Our approach makes use of support vector regression models, one of the state-of-the art techniques in the field of machine learning, to build effective predictors for single wind turbines based on data given for neighbored turbines.
- KonferenzbeitragWind Power Prediction with Cross-Correlation Weighted Nearest Neighbors(Proceedings of the 28th Conference on Environmental Informatics - Informatics for Environmental Protection, Sustainable Development and Risk Management, 2014) Treiber, Nils André; Kramer, OliverA precise wind power prediction is important for the integration of wind energy into the power grid. Besides numerical weather models for short-term predictions, there is a trend towards the development of statistical data-driven models that can outperform the classical forecast models [1]. In this paper, we improve a statistical prediction model proposed by Kramer and Gieseke [5], by employing a cross-correlation weighted k-nearest neighbor regression model (x-kNN). We demonstrate its superior performance by the comparison with the standard u-kNN method. Even if different pre-processing steps are considered, our regression technique achieves a comparably high accuracy.