Open Access
| Issue |
EPJ Web Conf.
Volume 330, 2025
The 5th International Conference on Electrical Sciences and Technologies in the Maghreb (CISTEM 2024)
|
|
|---|---|---|
| Article Number | 03005 | |
| Number of page(s) | 5 | |
| Section | Power Systems, Renewable Energy Systems, and Smart Grids | |
| DOI | https://doi.org/10.1051/epjconf/202533003005 | |
| Published online | 30 June 2025 | |
- Mounir, Short-term electric load forecasting using an EMD-BI-LSTM approach for smart grid energy management system. Journal Energy and Buildings, 10 (2023). https://www.sciencedirect.com/science/article/ abs/pii/S0378778823002529 [Google Scholar]
- Ke Yan, Multi-step short-term power consumption forecasting with a hybrid deep learning strategy. Journal Energies, 15 (2018). https://www.mdpi.com/ 1996-1073/11/11/3089 [Google Scholar]
- Tom B. Brown, Language models are few-shot learners. Journal Advances in neural information processing systems, (2020). https://arxiv.org/abs/2005.14165 [Google Scholar]
- Wang, Hydrological time series forecast model based on wavelet de-noising and ARIMA-LSTM (IEEE, 2019) [Google Scholar]
- Shahzad Muzaffar, Short-Term Load Forecasts Using LSTM Networks. Journal Energy Procedia, 6 (2019). https://www.sciencedirect.com/science/article/pii/S1876610219310008 [Google Scholar]
- Book Author, Consumption Data for 14 Consecutive Years Online .csv file, https://raw.githubusercontent.com/drwiiche/electricity-consumption/master/AEP_hourly.csv [Google Scholar]
- soumil shah, Data Analysis and Visualizations and Predicting Future Energy Consumption using LSTM Predicting Values 2 month Later Accurately RNN Online, 2019, https://soumilshah1995.blogspot.com/2019/ 08/project-data-analysis-and.html [Google Scholar]
- data world, Downing Street Project Online, https:// data.world/datasets/open-data [Google Scholar]
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