Renewable Energy Forecasting Using Deep Learning Models-Reference-Cited by-同舟云学术

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Renewable Energy Forecasting Using Deep Learning Models

Published:2023 Issue: Volume: Page:13-39
ISSN:2520-193X
Container-title:STEAM-H: Science, Technology, Engineering, Agriculture, Mathematics & Health
language:en
Short-container-title:

Author:

Malakar Sourav,Goswami Saptarsi,Ganguli Bhaswati,Chakrabarti Amlan

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-41352-0_2

Reference47 articles.

1. Abdel-Nasser, M., & Mahmoud, K. (2019). Accurate photovoltaic power forecasting models using deep LSTM-RNN. Neural Computing and Applications, 31(7), 2727–2740.

2. Afridi, M. J., Ross, A., & Shapiro, E. M. (2018). On automated source selection for transfer learning in convolutional neural networks. Pattern Recognition, 73, 65–75.

3. Ali-Ou-Salah, H., Oukarfi, B., Bahani, K., & Moujabbir, M. (2021). A new hybrid model for hourly solar radiation forecasting using daily classification technique and machine learning algorithms. Mathematical Problems in Engineering. https://doi.org/10.1155/2021/6692626

4. Alzahrani, A., Shamsi, P., Dagli, C., & Ferdowsi, M. (2017a). Solar irradiance forecasting using deep neural networks. Procedia Computer Science, 114, 304–313.

5. Alzahrani, A., Shamsi, P., Dagli, C., & Ferdowsi, M. (2017b). Solar irradiance forecasting using deep neural networks. Procedia Computer Science, 114, 304–313. https://doi.org/10.1016/j.procs.2017.09.045

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1. Advanced Forecasting Techniques for Smart Grids to Enhance Energy Efficiency and Sustainability;Proceedings of the 2024 International Conference on Information Technology for Social Good;2024-09-04

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