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000878533 1001_ $$0P:(DE-Juel1)179250$$aKruse, Johannes$$b0$$eCorresponding author
000878533 245__ $$aPredictability of power grid frequency
000878533 260__ $$aNew York, NY$$bIEEE$$c2020
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000878533 500__ $$aAaditional funding not listed by the system:  European Union’s Horizon 2020 Research and Innovation Programme through the Marie Skłodowska-Curie Grant under Agreement 840825
000878533 520__ $$aThe power grid frequency is the central observable in power system control, as it measures thebalance of electrical supply and demand. A reliable frequency forecast can facilitate rapid control actions andmay thus greatly improve power system stability. Here, we develop a weighted-nearest-neighbour (WNN) predictor to investigate how predictable the frequency trajectories are. Our forecasts for up to one hourare more precise than averaged daily profiles and could increase the efficiency of frequency control actions.Furthermore, we gain an increased understanding of the specific properties of different synchronous areas byinterpreting the optimal prediction parameters (number of nearest neighbours, the prediction horizon, etc.)in terms of the physical system. Finally, prediction errors indicate the occurrence of exceptional externalperturbations. Overall, we provide a diagnostics tool and an accurate predictor of the power grid frequencytime series, allowing better understanding of the underlying dynamics.
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000878533 7001_ $$0P:(DE-HGF)0$$aSchäfer, Benjamin$$b1
000878533 7001_ $$0P:(DE-Juel1)162277$$aWitthaut, Dirk$$b2
000878533 773__ $$0PERI:(DE-600)2687964-5$$a10.1109/ACCESS.2020.3016477$$gVol. 8, p. 149435 - 149446$$p149435 - 149446$$tIEEE access$$v8$$x2169-3536$$y2020
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