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Identifying representative load time series for load flow calculations

: Henze, J.; Kneiske, T.; Braun, M.; Sick, B.


Woon, W.L.:
Data analytics for renewable energy integration. Informing the generation and distribution of renewable energy : 5th ECML PKDD Workshop, DARE 2017, Skopje, Macedonia, September 22, 2017; Revised selected papers
Cham: Springer International Publishing, 2017 (Lecture Notes in Computer Science 10691)
ISBN: 978-3-319-71642-8 (Print)
ISBN: 978-3-319-71643-5 (Online)
ISBN: 3-319-71642-5
International Workshop on Data Analytics for Renewable Energy Integration (DARE) <5, 2017, Skopje>
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) <2017, Skopje>
Fraunhofer IWES ()

Power system analyses increasingly use annual time series for temporal and spatial assessment of operational and also planning aspects. These analyses are often limited due to the computational time of the large amount of load flow calculation. By introducing algorithms which are capable of generating shorter and representative time series of measured load or power generation time series, the calculation time for load flow calculations can be reduced. We present a method which is capable of extracting features from the time series and use those features to create a representative time series. Furthermore, we show that our method is capable of maintaining the most important statistical features of the original time series by applying a Fisher-Pitman Permutation test.