The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Fine tuning support vector machines for short-term wind speed forecasting
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Citations
Cites
Fault diagnosis of low speed bearing based on relevance vector machine and support vector machine
Widodo, A., Kim, E. Y., Son, J.-D., Yang, B.-S., Tan, A. C. C., Gu, D.-S., Choi, B.-K., & Mathew, J. (2009) · Expert Systems with Applications · doi:10.1016/j.eswa.2008.09.033 · Cited by 324
Cited by
Liu, D., Niu, D., Wang, H., & Fan, L. (2014) · Renewable Energy · doi:10.1016/j.renene.2013.08.011 · Cited by 451
A data-driven multi-model methodology with deep feature selection for short-term wind forecasting
Feng, C., Cui, M., Hodge, B.-M., & Zhang, J. (2017) · Applied Energy · doi:10.1016/j.apenergy.2017.01.043 · Cited by 319
Wang, D., Luo, H., Grunder, O., Lin, Y., & Guo, H. (2017) · Applied Energy · doi:10.1016/j.apenergy.2016.12.134 · Cited by 312
Support vector machines in engineering: an overview
Salcedo‐Sanz, S., Rojo‐Álvarez, J. L., Martínez‐Ramón, M., & Camps‐Valls, G. (2014) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1125 · Cited by 301
Metadata
- Authors
- Zhou, J., Shi, J., & Li, G.
- Venue
- Energy Conversion and Management
- Publisher
- Elsevier BV
- Published
- 2011-04
- Volume
- 52
- Issue
- 4
- Pages
- 1990-1998
- ISSN
- 0196-8904 (ISSN-L)
- OpenAlex
- W2022912953
- Cited by
- 397
- References
- 1
- FWCI
- 11.36
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Fine tuning support vector machines for short-term wind speed forecasting. https://app.cochat.ai/papers/10.1016/j.enconman.2010.11.007
- The paper
- Zhou, J., Shi, J., & Li, G. (2011). Fine tuning support vector machines for short-term wind speed forecasting. Energy Conversion and Management. https://doi.org/10.1016/j.enconman.2010.11.007
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.
