The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
A review on the young history of the wind power short-term prediction
Open accessAbstract
This paper makes a brief review on 30 years of history of the wind power short-term prediction, since the first ideas and sketches on the theme to the actual state of the art on models and tools, giving emphasis to the most significant proposals and developments. The two principal lines of thought on short-term prediction (mathematical and physical) are indistinctly treated here and comparisons between models and tools are avoided, mainly because, on the one hand, a standard for a measure of performance is still not adopted and, on the other hand, it is very important that the data are exactly the same in order to compare two models (this fact makes it almost impossible to carry out a quantitative comparison between a huge number of models and methods). In place of a quantitative description, a qualitative approach is preferred for this review, remarking the contribution (and innovative aspect) of each model. On the basis of the review, some topics for future research are pointed out.
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Cites
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A description of the fifth-generation Penn State/NCAR Mesoscale Model (MM5)
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A comparison of various forecasting techniques applied to mean hourly wind speed time series
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Self-citationOn the market impact of wind energy forecasts
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Self-citationShort‐term Prediction—An Overview
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Self-citationWind power meteorology. Part I: climate and turbulence
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Cited by
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
Review on probabilistic forecasting of wind power generation
Zhang, Y., Wang, J., & Wang, X. (2014) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2014.01.033 · Cited by 646
Energy Forecasting: A Review and Outlook
Hong, T., Pinson, P., Wang, Y., Weron, R., Yang, D., & Zareipour, H. (2020) · IEEE Open Access Journal of Power and Energy · doi:10.1109/oajpe.2020.3029979 · Cited by 621
Deep Learning for solar power forecasting — An approach using AutoEncoder and LSTM Neural Networks
Gensler, A., Henze, J., Sick, B., & Raabe, N. (2016) · 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC) · doi:10.1109/smc.2016.7844673 · Cited by 614
Ensemble methods for wind and solar power forecasting—A state-of-the-art review
Ren, Y., Suganthan, P. N., & Srikanth, N. (2015) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2015.04.081 · Cited by 435
Metadata
- Authors
- Costa, A., Crespo, A., Navarro, J., Lizcano, G., Madsen, H., & Feitosa, E.
- Venue
- Renewable and Sustainable Energy Reviews
- Publisher
- Elsevier BV
- Published
- 2008-08
- Volume
- 12
- Issue
- 6
- Pages
- 1725-1744
- ISSN
- 1364-0321 (ISSN-L)
- OpenAlex
- W2074511771
- Cited by
- 638
- References
- 10
- FWCI
- 20.79
- Type
- journal article
- Language
- en
- Open access
- green
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- Costa, A., Crespo, A., Navarro, J., Lizcano, G., Madsen, H., & Feitosa, E. (2008). A review on the young history of the wind power short-term prediction. Renewable and Sustainable Energy Reviews. https://doi.org/10.1016/j.rser.2007.01.015
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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