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All issues Volume 185 (2020) E3S Web Conf., 185 (2020) 01032 References
Open Access
Issue
E3S Web Conf.
Volume 185, 2020
2020 International Conference on Energy, Environment and Bioengineering (ICEEB 2020)
Article Number 01032
Number of page(s) 6
Section Energy Engineering and Power System
DOI https://doi.org/10.1051/e3sconf/202018501032
Published online 01 September 2020
  1. Shen F., Li Z., Shi X. (2020) Singular Perturbation for the Dynamic Modeling of Integrated Energy Systems. IEEE Transactions On Power Systems, 35:1718–1728. [CrossRef] [Google Scholar]
  2. Vaghefi A., Jafari M., Emmanuel B. (2014) Modeling and forecasting of cooling and electricity load demand. Applied Energy, 136:186–196. [Google Scholar]
  3. Kody M., Akshay S., Wesley J. (2014) Heating cooling and electrical load forecasting for a large- scale district energy system. Energy, 74: 877–885. [CrossRef] [Google Scholar]
  4. Vladimir V.M. (2019) Prediction of Heating Load and Cooling Load of Buildings Using Neural Network. International Ural Conference on Electrical Power Engineering. Chelyabinskaya Oblast, Russia. pp. 417–421. [Google Scholar]
  5. Nnamdi I.N. (2017) An artificial neural network model for predicting building heating and cooling loads. International Artificial Intelligence and Data Processing Symposium (IDAP). Malatya, Turkey. pp.17–22. [Google Scholar]
  6. Chourasia V.S., Mittra A.K. (2010)Wavelet-based denoising of fetal phonocardiographic signals. International Journal of Medical Engineering ＆ Informatics., 2:139–150. [CrossRef] [Google Scholar]
  7. Szegdy C., Liu W., Jia Y. (2015) Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Boston, USA. pp. 1–9. [Google Scholar]
  8. Chen L. (2014) A Fair Comparison Should Be Based on the Same Protocol—Comments on “Trainable Convolution Filters and Their Application to Face Recognition”. IEEE Transactions On Pattern Analysis And Machine Intelligence, 36: 622–623. [CrossRef] [PubMed] [Google Scholar]
  9. Ossama A., Mohamed A., Jiang H. (2012) Applying convolutional neural networks concepts to hybrid NN-HMM model for speech recognition. Proceeding of the IEEE International Conference on Acoustics, Speech and Signal Processing(ICASSP). Kyoto, Japan. pp. 4227–4280. [Google Scholar]
  10. Hochreiter S., Bengio Y., Frasconi P., Schmidhuber J. (2001) Gradient flow in recurrent nets: the difficulty of learning long-term dependencies: a field guide to dynamical recurrent neural networks. IEEE Press. 10:237–243. [Google Scholar]
  11. Lore K.G., Stoecklein D., Davies M. (2017) A deep learning framework for causal shape transformation. Neural Networks.98:305–317. [CrossRef] [Google Scholar]
  12. Medennikov I., Bulusheva A. (2016)LSTM-Based Language Models for Spontaneous Speech Recognition. International Conference on Speech & Computer. Okayama, Japan. pp. 469–475. [Google Scholar]
  13. Kong W., Zhao Y., David J.H. (2019) Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network. IEEE Transactions On Smart Grid. 13: 2062–2069. [Google Scholar]
  14. Sun Z., Zhao S., Zhang J. (2019) Short-Term Wind Power Forecasting on Multiple Scales Using VMD Decomposition, K-Means Clustering and LSTM Principal Computing. IEEE ACCESS.7: 166917–166929. [Google Scholar]
  15. Gers F., Schmidhuber J. (1997) Cummins F. Long short-term memory. Neural Computation. 9: 1735–1780. [Google Scholar]

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