The DOI matches its Crossref record; 141 of the 141 references it cites were found in a registry or an index.
A comprehensive review on deep learning approaches for short-term load forecasting
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Citations
Cites
1 retracted
Islam, B. U., Rasheed, M., & Ahmed, S. F. (2022) · Mathematical Problems in Engineering · doi:10.1155/2022/4049685 · Cited by 27
RetractedHochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1412.3555 · Cited by 11,008
Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M. A., Al-Amidie, M., & Farhan, L. (2021) · Journal of Big Data · doi:10.1186/s40537-021-00444-8 · Cited by 7,906
A review of intervention studies aimed at household energy conservation
Abrahamse, W., Steg, L., Vlek, C., & Rothengatter, T. (2005) · Journal of Environmental Psychology · doi:10.1016/j.jenvp.2005.08.002 · Cited by 2,668
Demand response and smart grids – a survey
Siano, P. (2014) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2013.10.022 · Cited by 2,411
Neural networks for short-term load forecasting: a review and evaluation
Hippert, H. S., Pedreira, C. E., & Souza, R. C. (2001) · IEEE Transactions on Power Systems · doi:10.1109/59.910780 · Cited by 2,221
Fundamentals of Power System Economics
Kirschen, D., & Strbac, G. (2004) · Wiley eBooks · doi:10.1002/0470020598 · Cited by 1,610
Metadata
- Authors
- Eren, Y., & Küçükdemiral, İ.
- Venue
- Renewable and Sustainable Energy Reviews
- Publisher
- Elsevier BV
- Published
- 2024-01
- Volume
- 189
- Pages
- 114031
- ISSN
- 1364-0321 (ISSN-L)
- OpenAlex
- W4388550577
- Cited by
- 303
- References
- 141
- FWCI
- 22.21
- Type
- journal article
- Language
- en
- Open access
- green
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- CoChat. (2026). Citation check: A comprehensive review on deep learning approaches for short-term load forecasting. https://app.cochat.ai/papers/10.1016/j.rser.2023.114031
- The paper
- Eren, Y., & Küçükdemiral, İ. (2024). A comprehensive review on deep learning approaches for short-term load forecasting. Renewable and Sustainable Energy Reviews. https://doi.org/10.1016/j.rser.2023.114031
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.
