The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
A Critical Review of Wind Power Forecasting Methods—Past, Present and Future
Open accessAbstract
The largest obstacle that suppresses the increase of wind power penetration within the power grid is uncertainties and fluctuations in wind speeds. Therefore, accurate wind power forecasting is a challenging task, which can significantly impact the effective operation of power systems. Wind power forecasting is also vital for planning unit commitment, maintenance scheduling and profit maximisation of power traders. The current development of cost-effective operation and maintenance methods for modern wind turbines benefits from the advancement of effective and accurate wind power forecasting approaches. This paper systematically reviewed the state-of-the-art approaches of wind power forecasting with regard to physical, statistical (time series and artificial neural networks) and hybrid methods, including factors that affect accuracy and computational time in the predictive modelling efforts. Besides, this study provided a guideline for wind power forecasting process screening, allowing the wind turbine/farm operators to identify the most appropriate predictive methods based on time horizons, input features, computational time, error measurements, etc. More specifically, further recommendations for the research community of wind power forecasting were proposed based on reviewed literature.
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Citations
Cites
The role of renewable energy in the global energy transformation
Gielen, D., Boshell, F., Saygin, D., Bazilian, M. D., Wagner, N., & Gorini, R. (2019) · Energy Strategy Reviews · doi:10.1016/j.esr.2019.01.006 · Cited by 4,973
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
A review of wind power and wind speed forecasting methods with different time horizons
Soman, S. S., Zareipour, H., Malik, O., & Mandal, P. (2010) · North American Power Symposium 2010 · doi:10.1109/naps.2010.5619586 · Cited by 875
Current status and future advances for wind speed and power forecasting
Jung, J., & Broadwater, R. (2014) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2013.12.054 · Cited by 710
An Advanced Statistical Method for Wind Power Forecasting
Sideratos, G., & Hatziargyriou, N. D. (2007) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2006.889078 · Cited by 627
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
A Review of Wind Power Forecasting Models
Wang, X., Guo, P., & Huang, X. (2011) · Energy Procedia · doi:10.1016/j.egypro.2011.10.103 · Cited by 443
Improvements in wind speed forecasts for wind power prediction purposes using Kalman filtering
Louka, P., Galanis, G., Siebert, N., Kariniotakis, G., Katsafados, P., Pytharoulis, I., & Kallos, G. (2008) · Journal of Wind Engineering and Industrial Aerodynamics · doi:10.1016/j.jweia.2008.03.013 · Cited by 409
Wind turbine condition monitoring by the approach of SCADA data analysis
Yang, W., Court, R., & Jiang, J. (2013) · Renewable Energy · doi:10.1016/j.renene.2012.11.030 · Cited by 400
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Cited by
Lipu, M. S. H., Miah, M. S., Hannan, M. A., Hussain, A., Sarker, M. R., Ayob, A., Saad, M. H. M., & Mahmud, M. S. (2021) · IEEE Access · doi:10.1109/access.2021.3097102 · Cited by 121
Metadata
- Authors
- Hanifi, S., Liu, X., Lin, Z., & Lotfian, S.
- Venue
- Energies
- Publisher
- MDPI AG
- Published
- 2020-07-22
- Volume
- 13
- Issue
- 15
- Pages
- 3764
- ISSN
- 1996-1073 (ISSN-L)
- OpenAlex
- W3044963726
- Cited by
- 435
- References
- 38
- FWCI
- 15.11
- Type
- journal article
- Language
- en
- Open access
- gold
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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.