| Session | ||
W15: Machine learning applications in power system
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| Presentations | ||
LSTM-based Active and Reactive Load Forecasting and its Replicability in Large Geographical Areas 1University of Zenica, Bosnia and Herzegovina; 2University of Manchester, UK; 3University of Tuzla, Bosnia and Herzegovina Evaluating the Impact of Data Availability on Machine Learning-augmented MPC for a Building Energy Management System 1Honda Research Institute Europe GmbH, Germany; 2Control Methods and Intelligent Systems Laboratory, Technical University of Darmstadt Scalable and Lightweight Machine Learning Based Load Forecast: Netload versus Disaggregrated Forecast Chalmers University of Technology, Sweden Machine Learning-based Model to Estimate the Dynamic Hosting Capacity in Distribution Network EELab/Lemcko, Department of Electromechanical, Systems and Metal Engineering, Ghent University, Kortrijk, Belgium Machine Learning-Driven Prediction of Load Shedding During Cascading Outages 1Department of Electrical and Computer Engineering, University of Cyprus, Nicosia, Cyprus; 2School of Technology, Woxsen University, Telangana, India Pioneering Roadmap for ML-Driven Algorithmic Advancements in Electrical Networks 1Delft University of Technology, Netherlands; 2Austrian Institute of Technology, Austria; 3Electric Power Research Institute, Ireland; 4nstitute for Systems and Computer Engineering, Technology and Science, Portugal; 5Hitachi Energy, Germany; 6The University of Manchester, United Kingdom; 7Energy Systems Catapult, United Kingdom; 8Arizona State University, United States of America; 9Réseau de Transport d'Électricité, France Play With Me: Towards Explaining the Benefits of Autocurriculum Training of Learning Agents 1Carl von Ossietzky University Oldenburg, Germany; 2OFFIS - Institute for Information Technology | ||