K-nearest neighbors for GEFCom2014 probabilistic wind power forecasting
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DOI: 10.1016/j.ijforecast.2015.11.007
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References listed on IDEAS
- Mangalova, E. & Agafonov, E., 2014. "Wind power forecasting using the k-nearest neighbors algorithm," International Journal of Forecasting, Elsevier, vol. 30(2), pages 402-406.
- Hong, Tao & Pinson, Pierre & Fan, Shu, 2014.
"Global Energy Forecasting Competition 2012,"
International Journal of Forecasting, Elsevier, vol. 30(2), pages 357-363.
- Tao Hong & Pierre Pinson & Shu Fan, 2013. "Global Energy Forecasting Competition 2012," HSC Research Reports HSC/13/16, Hugo Steinhaus Center, Wroclaw University of Technology.
- Hong, Tao & Pinson, Pierre & Fan, Shu & Zareipour, Hamidreza & Troccoli, Alberto & Hyndman, Rob J., 2016. "Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond," International Journal of Forecasting, Elsevier, vol. 32(3), pages 896-913.
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Cited by:
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- Dai, Xiaoran & Liu, Guo-Ping & Hu, Wenshan, 2023. "An online-learning-enabled self-attention-based model for ultra-short-term wind power forecasting," Energy, Elsevier, vol. 272(C).
- Liu, Yin & Davanloo Tajbakhsh, Sam & Conejo, Antonio J., 2021. "Spatiotemporal wind forecasting by learning a hierarchically sparse inverse covariance matrix using wind directions," International Journal of Forecasting, Elsevier, vol. 37(2), pages 812-824.
- Ma, Long & Huang, Ling & Shi, Huifeng, 2023. "A novel spatial–temporal generative autoencoder for wind speed uncertainty forecasting," Energy, Elsevier, vol. 282(C).
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Keywords
Probabilistic forecasting; Nonparametric smoothing; Quantile estimation; Distance metric; Optimization;All these keywords.
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