Open Access
| Issue |
E3S Web Conf.
Volume 136, 2019
2019 International Conference on Building Energy Conservation, Thermal Safety and Environmental Pollution Control (ICBTE 2019)
|
|
|---|---|---|
| Article Number | 01012 | |
| Number of page(s) | 4 | |
| Section | Ultra-Low Energy Consumption Building Technology | |
| DOI | https://doi.org/10.1051/e3sconf/201913601012 | |
| Published online | 10 December 2019 | |
- Xiaoxia Ge, Hongchuang Xiao, et al. (2018) Vacuum prediction of condenser based on generalized regression neural network optimized by fruit fly algorithm. Turbine Technology, 60: 208-2012. [Google Scholar]
- Jianqiang Li, Xingxu Chen, et al. Target value prediction of condenser vacuum degree based on ps0-svr model. Electric Power Science and Engineering, 2017(2):66-72. [Google Scholar]
- Liping Zhang, Haotian Chen, Fengwei Wang, et al. (2015) Application of PSO-algorithm modified Elman neural network in vacuum prediction for dual pressure condensers. Thermal Power Generation, 3:53-57. [Google Scholar]
- Jianguo Wang, Leping Lin, et al. (2015) A vacuum value prediction method for steam condensers using RBF neural network optimized by particle swarm algorithm. Thermal Power Generation., 10:72-76. [Google Scholar]
- Hai Zhang, Jian Pu, et al. (2010) A model for predicting vacuum in the condenser based on Elman neural network by using particle swarm optimization algorithm. Thermal Power Generation, 4: 7-11. [Google Scholar]
- Hochreiter, Sepp, Jürgen s. (1997) Long short-term memory Neural Computation., 9: 1735-1780. [CrossRef] [PubMed] [Google Scholar]
- Graves, Alex, Navdeep J, et al. (2013) Hybrid speech recognition with deep bidirectional LSTM. In Automatic Speech Recognition and Understanding (ASRU), 273-278. [Google Scholar]
- Fan Xu, Hua Cheng, Yiquan Fang. (2017) A Gait pattern classification method based on CLSTM [J]. Journal of East China University of Science and Technology (Natural Science Edition), 43: 553-8. [Google Scholar]
- Daniel L M, Kasun A, Milos M. (2016) Building energy load forecasting using deep neural networks. In: Industrial Electronics Society, IECON 2016-42nd Annual Conference of the IEEE, 7046-7051. [Google Scholar]
- Huiting Zheng, et al. (2017) Short-term load forecasting using EMD-LSTM neural networks with a Xgboost algorithm for feature importance evaluation. Energies, 10:1168. [CrossRef] [Google Scholar]
- Goodfellow Ian, Bengio Yoshua, Courville Aaron. Deep learning. (2016) Cambridge, Massachusetts: MIT Press. [Google Scholar]
- Han J, Moraga C. (1995) The influence of the sigmoid function parameters on the speed of backpropagation learning. In: International Workshop on Artificial Neural Networks, 195-201. [Google Scholar]
- Chang Yu. (2014) Analyzes of the condenser vacuum degree prediction based on a 660MW power plant in China. Huazhong University of Science & Technology. [Google Scholar]
- Kingma, Diederik, and Jimmy Ba. (2014) ADAM: A method for stochastic optimization. In: International Conference on Learning Representations (ICLR). [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.