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All issues Volume 217 (2019) EPJ Web Conf., 217 (2019) 01002 References
Open Access
Issue
EPJ Web Conf.
Volume 217, 2019
International Workshop on Flexibility and Resiliency Problems of Electric Power Systems (FREPS 2019)
Article Number 01002
Number of page(s) 6
DOI https://doi.org/10.1051/epjconf/201921701002
Published online 15 October 2019
  1. A. Monticelli, State estimation in electric power systems: a generalized approach. – Springer Science & Business Media, 2012. [Google Scholar]
  2. A.Z. Gamm, Statistical methods for state estimation of electric power systems. – Nauka, 1976. [Google Scholar]
  3. D. L. Marino, K. Amarasinghe, M. Manic, Building energy load forecasting using deep neural networks //Industrial Electronics Society, IECON 2016-42nd Annual Conference of the IEEE. – IEEE, 2016. – C. 7046-7051. [Google Scholar]
  4. R. Jozefowicz, W. Zaremba, I. Sutskever, An empirical exploration of recurrent network architectures //International Conference on Machine Learning. – 2015. – C. 2342-2350. [Google Scholar]
  5. A. Graves, Practical variational inference for neural networks //Advances in neural information processing systems. – 2011. – C. 2348-2356. [Google Scholar]
  6. C. Blundell et al., Weight uncertainty in neural networks //arXiv preprint arXiv:1505.05424. – 2015. [Google Scholar]
  7. M.J. Wainwright et al., Graphical models, exponential families, and variational inference //Foundations and Trends® in Machine Learning. – 2008. – Т. 1. – №. 1–2. – C. 1-305. [Google Scholar]
  8. A. Graves, Practical variational inference for neural networks //Advances in neural information processing systems. – 2011. – C. 2348-2356. [Google Scholar]
  9. C. Blundell et al., Weight uncertainty in neural networks //arXiv preprint arXiv:1505.05424. – 2015. [Google Scholar]
  10. M. Fortunato, C. Blundell, O. Vinyals, Bayesian recurrent neural networks //arXiv preprint arXiv:1704.02798. – 2017. [Google Scholar]
  11. T.K. Ho, Random decision forests //Proceedings of 3rd international conference on document analysis and recognition. IEEE, 1995. Т. 1. C. 278-282. [Google Scholar]
  12. L. Breiman, Random forests //Machine learning. 2001. Т. 45. №. 1. C. 5-32. [Google Scholar]
  13. E.M. Kleinberg, On the algorithmic implementation of stochastic discrimination //IEEE Transactions on Pattern Analysis and Machine Intelligence. 2000. Т. 22. №. 5. C. 473-490. [Google Scholar]
  14. A. Domyshev et al., Optimal Power Flow Calculation Using BFGS-Based Optimisation Scheme //2018 2nd IEEE Conference on Energy Internet and Energy System Integration (EI2). IEEE, 2018. C. 1-6. [Google Scholar]
  15. R.H. Byrd et al., A limited memory algorithm for bound constrained optimization //SIAM Journal on Scientific Computing. 1995. Т. 16. №. 5. C. 1190-1208. [CrossRef] [MathSciNet] [Google Scholar]
  16. P. Armand, P. Segalat, A limited memory algorithm for inequality constrained minimization. Technical Report 2003-08, University of Limoges (France) 2003, 2003. [Google Scholar]
  17. Software package for power system modeling, [Online]. Available in RUSSIAN: http://anares.ru/software [Google Scholar]

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