The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
A data-driven multi-model methodology with deep feature selection for short-term wind forecasting
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Cites
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
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Recursive feature elimination with random forest for PTR-MS analysis of agroindustrial products
Granitto, P. M., Furlanello, C., Biasioli, F., & Gasperi, F. (2006) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2006.01.007 · Cited by 663
Liu, D., Niu, D., Wang, H., & Fan, L. (2014) · Renewable Energy · doi:10.1016/j.renene.2013.08.011 · Cited by 451
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
Review of the use of Numerical Weather Prediction (NWP) Models for wind energy assessment
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Liu, H., Tian, H.-Q., Pan, D.-F., & Li, Y.-F. (2013) · Applied Energy · doi:10.1016/j.apenergy.2013.02.002 · Cited by 334
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2016 European Control Conference (ECC)
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Cited by
Lago, J., De Ridder, F., & De Schutter, B. (2018) · Applied Energy · doi:10.1016/j.apenergy.2018.02.069 · Cited by 642
Deep belief network based ensemble approach for cooling load forecasting of air-conditioning system
Fu, G. (2018) · Energy · doi:10.1016/j.energy.2018.01.180 · Cited by 181
Suryanarayana, G., Lago, J., Geysen, D., Aleksiejuk, P., & Johansson, C. (2018) · Energy · doi:10.1016/j.energy.2018.05.111 · Cited by 155
Allal, Z., Noura, H. N., Salman, O., & Chahine, K. (2024) · Journal of Environmental Management · doi:10.1016/j.jenvman.2024.120392 · Cited by 130
Metadata
- Authors
- Feng, C., Cui, M., Hodge, B.-M., & Zhang, J.
- Venue
- Applied Energy
- Publisher
- Elsevier BV
- Published
- 2017-03
- Volume
- 190
- Pages
- 1245-1257
- ISSN
- 0306-2619 (ISSN-L)
- OpenAlex
- W2581205918
- Cited by
- 319
- References
- 34
- FWCI
- 17.90
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Feng, C., Cui, M., Hodge, B.-M., & Zhang, J. (2017). A data-driven multi-model methodology with deep feature selection for short-term wind forecasting. Applied Energy. https://doi.org/10.1016/j.apenergy.2017.01.043
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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.
