The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Short-term wind speed forecasting using wavelet transform and support vector machines optimized by genetic algorithm
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Citations
Cites
Some recent development in a concept of causality
Granger, C. W. J. (1988) · Journal of Econometrics · doi:10.1016/0304-4076(88)90045-0 · Cited by 2,945
Current methods and advances in forecasting of wind power generation
Foley, A. M., Leahy, P. G., Marvuglia, A., & McKeogh, E. J. (2012) · Renewable Energy · doi:10.1016/j.renene.2011.05.033 · Cited by 1,317
Day-ahead wind speed forecasting using f-ARIMA models
Kavasseri, R. G., & Seetharaman, K. (2009) · Renewable Energy · doi:10.1016/j.renene.2008.09.006 · Cited by 951
Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model
Guo, Z., Zhao, W., Lu, H., et al. (2011) · Renewable Energy · doi:10.1016/j.renene.2011.06.023 · Cited by 530
Fine tuning support vector machines for short-term wind speed forecasting
Zhou, J., Shi, J., & Li, G. (2011) · Energy Conversion and Management · doi:10.1016/j.enconman.2010.11.007 · Cited by 397
Short term wind speed forecasting in La Venta, Oaxaca, México, using artificial neural networks
Cadenas, E., & Rivera, W. (2009) · Renewable Energy · doi:10.1016/j.renene.2008.03.014 · Cited by 312
Liu, H., Erdem, E., & Shi, J. (2010) · Applied Energy · doi:10.1016/j.apenergy.2010.09.028 · Cited by 214
Wind speed forecasting in the South Coast of Oaxaca, México
Cadenas, E., & Rivera, W. (2006) · Renewable Energy · doi:10.1016/j.renene.2006.10.005 · Cited by 212
Jursa, R., & Rohrig, K. (2008) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2008.08.007 · Cited by 198
A soft computing system for day-ahead electricity price forecasting
Niu, D., Liu, D., & Wu, D. D. (2009) · Applied Soft Computing · doi:10.1016/j.asoc.2009.10.004 · Cited by 60
Self-citation
Cited by
Liu, H., Mi, X.-W., & Li, Y.-F. (2018) · Energy Conversion and Management · doi:10.1016/j.enconman.2017.11.053 · Cited by 474
Applications of hybrid models in chemical, petroleum, and energy systems: A systematic review
Zendehboudi, S., Rezaei, N., & Lohi, A. (2018) · Applied Energy · doi:10.1016/j.apenergy.2018.06.051 · Cited by 394
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
- Liu, D., Niu, D., Wang, H., & Fan, L.
- Venue
- Renewable Energy
- Publisher
- Elsevier BV
- Published
- 2014-02
- Volume
- 62
- Pages
- 592-597
- ISSN
- 0960-1481 (ISSN-L)
- OpenAlex
- W1995140642
- Cited by
- 451
- References
- 12
- FWCI
- 13.18
- Type
- journal article
- Language
- en
- Open access
- closed
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- Liu, D., Niu, D., Wang, H., & Fan, L. (2014). Short-term wind speed forecasting using wavelet transform and support vector machines optimized by genetic algorithm. Renewable Energy. https://doi.org/10.1016/j.renene.2013.08.011
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.
