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Patrick Glauner

On the Reduction of Biases in Big Data Sets for the Detection of Irregular Power Usage



Patrick Glauner , Radu State , Petko Valtchev , Diogo Duarte

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* Proceedings of the 13th International FLINS Conference on Data Science and Knowledge Engineering for Sensing Decision Support (FLINS 2018) 
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Impact of Biases in Big Data



Patrick Glauner , Petko Valtchev , Radu State

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* Proceedings of the 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018) 
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Identifying Irregular Power Usage by Turning Predictions into Holographic Spatial Visualizations



Patrick Glauner , Niklas Dahringer , Oleksandr Puhachov , Jorge Augusto Meira , Petko Valtchev , Radu State , Diogo Duarte

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* Proceedings of the 17th IEEE International Conference on Data Mining Workshops (ICDMW 2017) 
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Neighborhood Features Help Detecting Non-Technical Losses in Big Data Sets



Patrick Glauner , Jorge Meira , Lautaro Dolberg , Radu State , Franck Bettinger , Yves Rangoni , Diogo Duarte

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* Proceedings of the 3rd IEEE/ACM International Conference on Big Data Computing Applications and Technologies (BDCAT 2016) 
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Is Big Data Sufficient for a Reliable Detection of Non-Technical Losses?



Patrick Glauner , Angelo Migliosi , Jorge Meira , Petko Valtchev , Radu State , Franck Bettinger

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* Proceedings of the 19th International Conference on Intelligent System Applications to Power Systems (ISAP 2017) 
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The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey



Patrick Glauner , Jorge Augusto Meira , Petko Valtchev , Radu State , Franck Bettinger

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* International Journal of Computational Intelligence Systems (IJCIS), vol. 10, issue 1, pp. 760-775, 2017 
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The Top 10 Topics in Machine Learning Revisited: A Quantitative Meta-Study



Patrick Glauner , Manxing Du , Victor Paraschiv , Andrey Boytsov , Isabel Lopez Andrade , Jorge Meira , Petko Valtchev , Radu State

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* Proceedings of the 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2017) 
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