Open Access
| Issue |
E3S Web Conf.
Volume 466, 2023
2023 8th International Conference on Advances in Energy and Environment Research & Clean Energy and Energy Storage Technology Forum (ICAEER & CEEST 2023)
|
|
|---|---|---|
| Article Number | 02001 | |
| Number of page(s) | 5 | |
| Section | Green Energy Technology and Low Carbon Energy Saving Strategy | |
| DOI | https://doi.org/10.1051/e3sconf/202346602001 | |
| Published online | 15 December 2023 | |
- Olawumi T O,Chan D W M. A scientometric review of global research on sustainability and sustainable development[J]. Journal of Cleaner Production, 2018(183): 23 1-250. [Google Scholar]
- Wang Wei, Xu Jing, Zhao Xiang, et al. Peak shaving operation status analysis of coal-fired power units in China [J ]. Southern Energy Construction, 2017, 4(1): 18-24. [Google Scholar]
- Zhenpeng H, Zixuan W, Jianlin L, et al. A novel capacity demand analysis method of energy storage system for peak shaving based on data-driven[J]. Journal of Energy Storage, 2021, 39:102617. [CrossRef] [Google Scholar]
- Analysis of energy storage demand for peak shaving and frequency regulation of power systems with high penetration of renewable energy [Google Scholar]
- Hierarchical scheduling learning optimisation of two-area active distribution system considering peak shaving demand of power grid [Google Scholar]
- A. Gasparin, S. Lukovic, C. Alippi, Deep learning for time series forecasting: The electric load case, CAAI Trans. Intell. Technol. 7 (1) (2022) 1–25. [Google Scholar]
- E. Tuna and A. Soysal, “LSTM and GRU based Traffic Prediction Using Live Network Data,” 2021 29th Signal Processing and Communications Applications Conference (SIU), Istanbul, Turkey, 2021, pp. 1-4 [Google Scholar]
- An improved LSTM-Seq2Seq-based forecasting method for electricity load [Google Scholar]
- Torres, J.F., Martínez-Álvarez, F. & Troncoso, A. A deep LSTM network for the Spanish electricity consumption forecasting. Neural Comput & Applic 34, 10533–10545 (2022). [CrossRef] [PubMed] [Google Scholar]
- Wind Speed Forecast using LSTM and Bi-LSTM Algorithms over Gabal El-Zayt Wind Farm [Google Scholar]
- Shi X , Chen Z , Wang H , et al. Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting[J].MIT Press, 2015. [Google Scholar]
- Li M , Chen G , Lin Z , et al. Combination Forecasting Model Based on Drift[C]//International Joint Conference on Computational Sciences & Optimization. IEEE Computer Society, 2009. [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.