The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Accurate photovoltaic power forecasting models using deep LSTM-RNN
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Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Speech recognition with deep recurrent neural networks
Graves, A., Mohamed, A.-R., & Hinton, G. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6638947 · Cited by 8,893
Smart Energy Systems for coherent 100% renewable energy and transport solutions
Mathiesen, B. V., Lund, H., Connolly, D., Wenzel, H., Østergaard, P. A., Möller, B., Nielsen, S., Ridjan, I., Karnøe, P., Sperling, K., et al. (2015) · Applied Energy · doi:10.1016/j.apenergy.2015.01.075 · Cited by 1,265
Photovoltaic and solar power forecasting for smart grid energy management
Wan, C., Zhao, J., Song, Y., Xu, Z., Lin, J., & Hu, Z. (2015) · CSEE Journal of Power and Energy Systems · doi:10.17775/cseejpes.2015.00046 · Cited by 654
Cited by
A review of green artificial intelligence: Towards a more sustainable future
Bolón-Canedo, V., Morán-Fernández, L., Cancela, B., & Alonso-Betanzos, A. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128096 · Cited by 486
Machine Learning and Deep Learning in smart manufacturing: The Smart Grid paradigm
Kotsiopoulos, T., Sarigiannidis, P., Ioannidis, D., & Tzovaras, D. (2021) · Computer Science Review · doi:10.1016/j.cosrev.2020.100341 · Cited by 341
LSTM-based indoor air temperature prediction framework for HVAC systems in smart buildings
Mtibaa, F., Nguyen, K.-K., Azam, M., Papachristou, A., Venne, J.-S., & Cheriet, M. (2020) · Neural Computing and Applications · doi:10.1007/s00521-020-04926-3 · Cited by 139
Split Federated Learning for 6G Enabled-Networks: Requirements, Challenges, and Future Directions
Hafi, H., Brik, B., Frangoudis, P. A., Ksentini, A., & Bagaa, M. (2024) · IEEE Access · doi:10.1109/access.2024.3351600 · Cited by 90
Metadata
- Authors
- Abdel-Nasser, M., & Mahmoud, K.
- Venue
- Neural Computing and Applications
- Published
- 2017-10-14
- Volume
- 31
- Issue
- 7
- Pages
- 2727-2740
- ISSN
- 0941-0643 (ISSN-L)
- 1433-3058
- OpenAlex
- W2760948241
- Cited by
- 752
- References
- 4
- FWCI
- 23.78
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Accurate photovoltaic power forecasting models using deep LSTM-RNN. https://app.cochat.ai/papers/10.1007/s00521-017-3225-z
- The paper
- Abdel-Nasser, M., & Mahmoud, K. (2019). Accurate photovoltaic power forecasting models using deep LSTM-RNN. Neural Computing and Applications. https://doi.org/10.1007/s00521-017-3225-z
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
