The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
Wind speed forecasting method based on deep learning strategy using empirical wavelet transform, long short term memory neural network and Elman neural network
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Convolutional Neural Networks for Sentence Classification
Kim, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1181 · Cited by 14,831
Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
Shi, X., Chen, Z., Wang, H., Yeung, D.-Y., Wong, W.-K., & Woo, W.-C. (2015) · arXiv · doi:10.48550/arxiv.1506.04214 · Cited by 7,709
Deep learning-based classification of hyperspectral data
Chen, Y., Lin, Z., Zhao, X., Wang, G., & Gu, Y. (2014) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2014.2329330 · Cited by 2,679
Gilles, J. (2013) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2013.2265222 · Cited by 2,210
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
A review of combined approaches for prediction of short-term wind speed and power
Tascikaraoglu, A., & Uzunoglu, M. (2014) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2014.03.033 · Cited by 674
Wang, S., Zhang, N., Wu, L., & Wang, Y. (2016) · Renewable Energy · doi:10.1016/j.renene.2016.03.103 · Cited by 674
Ren, C., An, N., Wang, J., Li, L., Hu, B., & Shang, D. (2014) · Knowledge-Based Systems · doi:10.1016/j.knosys.2013.11.015 · Cited by 530
Time series forecasting using a deep belief network with restricted Boltzmann machines
Kuremoto, T., Kimura, S., Kobayashi, K., & Obayashi, M. (2014) · Neurocomputing · doi:10.1016/j.neucom.2013.03.047 · Cited by 527
Deep belief network based deterministic and probabilistic wind speed forecasting approach
Wang, H. Z., Wang, G. B., Li, G. Q., Peng, J. C., & Liu, Y. T. (2016) · Applied Energy · doi:10.1016/j.apenergy.2016.08.108 · Cited by 495
Cited by
Wang, K., Qi, X., & Liu, H. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.113315 · Cited by 574
Self-citationArtificial Intelligence Enabled Demand Response: Prospects and Challenges in Smart Grid Environment
Khan, M. A., Saleh, A. M., Waseem, M., & Sajjad, I. A. (2023) · IEEE Access · doi:10.1109/access.2022.3231444 · Cited by 179
Metadata
- Authors
- Liu, H., Mi, X.-W., & Li, Y.-F.
- Venue
- Energy Conversion and Management
- Publisher
- Elsevier BV
- Published
- 2018-01
- Volume
- 156
- Pages
- 498-514
- ISSN
- 0196-8904 (ISSN-L)
- OpenAlex
- W2769156605
- Cited by
- 474
- References
- 28
- FWCI
- 16.89
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Wind speed forecasting method based on deep learning strategy using empirical wavelet transform, long short term memory neural network and Elman neural network. https://app.cochat.ai/papers/10.1016/j.enconman.2017.11.053
- The paper
- Liu, H., Mi, X.-W., & Li, Y.-F. (2018). Wind speed forecasting method based on deep learning strategy using empirical wavelet transform, long short term memory neural network and Elman neural network. Energy Conversion and Management. https://doi.org/10.1016/j.enconman.2017.11.053
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.
