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- W2970928015 abstract "In the past years, a rapid deployment of battery energy storage systems for diverse smart grid services has been seen in electric power systems. However, a cost-effective and multi-objective application of these services necessitates a utilization of forecasting methods for a development of efficient capacity allocation and risk management strategies over the uncertainty of battery state-of-charge. The aim of this paper is to assess the tuning efficiency of multi-attention recurrent neural network for multi-step forecasting of battery state-of-charge under provision of primary frequency control. In particular, this paper describes hyper-parameter optimization of the network with a tree-structured parzen estimator and compares such optimization performance with random and manual search on a simulated battery state-of-charge dataset. The experimental results demonstrate that the tree-structured parzen estimator enables 0.6% and 1.5% score improvement for the dataset compared with the random and manual search, respectively." @default.
- W2970928015 created "2019-09-05" @default.
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- W2970928015 date "2019-01-01" @default.
- W2970928015 modified "2023-09-26" @default.
- W2970928015 title "Hyper-parameter Optimization of Multi-attention Recurrent Neural Network for Battery State-of-Charge Forecasting" @default.
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- W2970928015 doi "https://doi.org/10.1007/978-3-030-30241-2_41" @default.
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