Hyper-parameter Optimization of Multi-attention Recurrent Neural Network for Battery State-of-Charge Forecasting
Mashlakov, Aleksei; Tikka, Ville; Lensu, Lasse; Romanenko, Aleksei; Honkapuro, Samuli (2019-08-30)
Post-print / Final draft
Mashlakov, Aleksei
Tikka, Ville
Lensu, Lasse
Romanenko, Aleksei
Honkapuro, Samuli
30.08.2019
482-494
Springer, Cham
School of Energy Systems
Kaikki oikeudet pidätetään.
© Springer Nature Switzerland AG 2019
© Springer Nature Switzerland AG 2019
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe2019112043240
https://urn.fi/URN:NBN:fi-fe2019112043240
Tiivistelmä
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.
Lähdeviite
Mashlakov, A., Tikka, V., Lensu, L., Romanenko, A., Honkapuro, S. (2019). Hyper-parameter Optimization of Multi-attention Recurrent Neural Network for Battery State-of-Charge Forecasting. In: Moura Oliveira, P., Novais, P., Reis, L. (Eds.) Progress in Artificial Intelligence. EPIA 2019. Lecture Notes in Computer Science, vol 11804. pp. 482-494. DOI: 10.1007/978-3-030-30241-2_41
Kokoelmat
- Tieteelliset julkaisut [1523]