The DOI matches its Crossref record; 49 of the 49 references it cites were found in a registry or an index.
Hybrid VMD-CNN-GRU-based model for short-term forecasting of wind power considering spatio-temporal features
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Citations
Cites
The Arithmetic Optimization Algorithm
Abualigah, L., Diabat, A., Mirjalili, S., Abd Elaziz, M., & Gandomi, A. H. (2021) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2020.113609 · Cited by 2,928
Aquila Optimizer: A novel meta-heuristic optimization algorithm
Abualigah, L., Yousri, D., Abd Elaziz, M., Ewees, A. A., Al-qaness, M. A. A., & Gandomi, A. H. (2021) · Computers & Industrial Engineering · doi:10.1016/j.cie.2021.107250 · Cited by 2,213
Predicting residential energy consumption using CNN-LSTM neural networks
Kim, T.-Y., & Cho, S.-B. (2019) · Energy · doi:10.1016/j.energy.2019.05.230 · Cited by 1,469
Reptile Search Algorithm (RSA): A nature-inspired meta-heuristic optimizer
Abualigah, L., Elaziz, M. A., Sumari, P., Geem, Z. W., & Gandomi, A. H. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.116158 · Cited by 1,433
Deep learning based ensemble approach for probabilistic wind power forecasting
Wang, H.-Z., Li, G.-Q., Wang, G.-B., Peng, J.-C., Jiang, H., & Liu, Y.-T. (2017) · Applied Energy · doi:10.1016/j.apenergy.2016.11.111 · Cited by 753
Altan, A., Karasu, S., & Zio, E. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106996 · Cited by 597
Prairie Dog Optimization Algorithm
Ezugwu, A. E., Agushaka, J. O., Abualigah, L., Mirjalili, S., & Gandomi, A. H. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07530-9 · Cited by 559
A Novel CNN-GRU-Based Hybrid Approach for Short-Term Residential Load Forecasting
Sajjad, M., Khan, Z. A., Ullah, A., Hussain, T., Ullah, W., Lee, M. Y., & Baik, S. W. (2020) · IEEE Access · doi:10.1109/access.2020.3009537 · Cited by 528
A survey of artificial neural network in wind energy systems
Marugán, A. P., Márquez, F. P. G., Perez, J. M. P., & Ruiz-Hernández, D. (2018) · Applied Energy · doi:10.1016/j.apenergy.2018.07.084 · Cited by 479
Short-term wind power forecasting based on support vector machine with improved dragonfly algorithm
Li, L.-L., Zhao, X., Tseng, M.-L., & Tan, R. R. (2020) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2019.118447 · Cited by 463
Cited by
Temperature Prediction of Mushrooms Based on a Data—Physics Hybrid Approach
Wang, M., Kong, X., Shan, F., Zheng, W., Ren, P., Wang, J., Chen, C., Zhang, X., & Zhao, C. (2024) · Agriculture · doi:10.3390/agriculture14010145 · Cited by 9
Metadata
- Authors
- Zhao, Z., Yun, S., Jia, L., Guo, J., Meng, Y., He, N., Li, X., Shi, J., & Yang, L.
- Venue
- Engineering Applications of Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 2023-05
- Volume
- 121
- Pages
- 105982
- ISSN
- 0952-1976 (ISSN-L)
- OpenAlex
- W4321485251
- Cited by
- 389
- References
- 49
- FWCI
- 28.51
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Hybrid VMD-CNN-GRU-based model for short-term forecasting of wind power considering spatio-temporal features. https://app.cochat.ai/papers/10.1016/j.engappai.2023.105982
- The paper
- Zhao, Z., Yun, S., Jia, L., Guo, J., Meng, Y., He, N., Li, X., Shi, J., & Yang, L. (2023). Hybrid VMD-CNN-GRU-based model for short-term forecasting of wind power considering spatio-temporal features. Engineering Applications of Artificial Intelligence. https://doi.org/10.1016/j.engappai.2023.105982
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.
