1. Abadi, M., et al.: Tensorflow: a system for large-scale machine learning. In: 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 2016), pp. 265–283 (2016)

   Google Scholar 

2. Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprintarXiv:1409.0473 (2014)

3. Belonogova, N., et al.: Methodology to define a BESS operating strategy for the end-customer in the changing business environment. In: 2018 15th International Conference on the European Energy Market (EEM), pp. 1–6. IEEE (2018)

   Google Scholar 

4. Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. J. Mach. Learn. Res.13(Feb), 281–305 (2012)

   MathSciNet MATH  Google Scholar 

5. Bergstra, J., Yamins, D., Cox, D.D.: Making a science of model search: hyperparameter optimization in hundreds of dimensions for vision architectures (2013)

   Google Scholar 

6. Bergstra, J.S., Bardenet, R., Bengio, Y., Kégl, B.: Algorithms for hyper-parameter optimization. In: Advances in Neural Information Processing Systems, pp. 2546–2554 (2011)

   Google Scholar 

7. Chollet, F., et al.: Keras (2015).https://keras.io

8. Chourey, D.R., Gupta, H., Kumar, A., Kumar, J., Kumar, A., Mishra, A.: Analyzing effect of system inertia on grid frequency forecasting using two stage neuro-fuzzy system. J. Inst. Eng. (India): Series B99(2), 125–136 (2018)

   Google Scholar 

9. Dong, D., Sheng, Z., Yang, T.: Wind power prediction based on recurrent neural network with long short-term memory units. In: 2018 International Conference on Renewable Energy and Power Engineering (REPE), pp. 34–38. IEEE (2018)

   Google Scholar 

10. Fitzgerald, G., Mandel, J., Morris, J., Touati, H.: The economics of battery energy storage: How multi-use, customer-sited batteries deliver the most services and value to customers and the grid. Rocky Mountain Institute, p. 6 (2015)

    Google Scholar 

11. Hesse, H., Schimpe, M., Kucevic, D., Jossen, A.: Lithium-ion battery storage for the grid a review of stationary battery storage system design tailored for applications in modern power grids. Energies10(12), 2107 (2017)

    Article  Google Scholar 

12. Hyperopt: Distributed asynchronous hyperparameter optimization in Python. (2012).https://hyperopt.github.io/hyperopt/. Accessed 30 Apr 2019

13. Kong, W., Dong, Z.Y., Jia, Y., Hill, D.J., Xu, Y., Zhang, Y.: Short-term residential load forecasting based on LSTM recurrent neural network. IEEE Trans. Smart Grid10, 841–851 (2017)

    Article  Google Scholar 

14. Muhammad, A., Lee, J.M., Hong, S.W., Lee, S.J., Lee, E.H.: Deep learning application in power system with a case study on solar irradiation forecasting. In: 2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), pp. 275–279. IEEE (2019)

    Google Scholar 

15. Müller, M., et al.: Evaluation of grid-level adaptability for stationary battery energy storage system applications in Europe. J. Energy Storage9, 1–11 (2017)

    Article  Google Scholar 

16. Obaid, Z.A., Cipcigan, L.M., Abrahim, L., Muhssin, M.T.: Frequency control of future power systems: reviewing and evaluating challenges and new control methods. J. Modern Power Syst. Clean Energy7(1), 9–25 (2019)

    Article  Google Scholar 

17. Pedersen, M.E.H.: Hyper-parameter optimization (2018).https://github.com/Hvass-Labs/TensorFlow-Tutorials

18. Petneházi, G.: Recurrent neural networks for time series forecasting. arXiv preprintarXiv:1901.00069 (2019)

19. Pumperla, M.: Hyperas: a very simple convenience wrapper around hyperopt for fast prototyping with keras models (2017).https://maxpumperla.com/hyperas/. Accessed 30 Apr 2019

20. Ratsimbazafy, M.: McKinsey SmartCities traffic prediction (2018).https://github.com/mratsim/McKinsey-SmartCities-Traffic-Prediction

21. RTE: Continental Europe synchronous area frequency data metered by RTE (2019).https://clients.rte-france.com. Accessed 30 Apr 2019

22. Shi, H., Xu, M., Li, R.: Deep learning for household load forecasting–a novel pooling deep RNN. IEEE Trans. Smart Grid9(5), 5271–5280 (2018)

    Article  Google Scholar 

23. Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, pp. 3104–3112 (2014)

    Google Scholar 

24. Toubeau, J.F., Bottieau, J., Vallée, F., De Grève, Z.: Deep learning-based multivariate probabilistic forecasting for short-term scheduling in power markets. IEEE Trans. Power Syst.34(2), 1203–1215 (2019)

    Article  Google Scholar 

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