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Wind power forecasting using the k-nearest neighbors algorithm

Author

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  • Mangalova, E.
  • Agafonov, E.

Abstract

The paper deals with a modeling procedure which aims to predict the power output of wind farm electricity generators. The following modeling steps are proposed: factor selection, raw data pretreatment, model evaluation and optimization. Both heuristic and formal methods are combined to construct the model. The basic modeling approach here is the k-nearest neighbors method.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:intfor:v:30:y:2014:i:2:p:402-406
    DOI: 10.1016/j.ijforecast.2013.07.008
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    Citations

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    Cited by:

    1. Nagy, Gábor I. & Barta, Gergő & Kazi, Sándor & Borbély, Gyula & Simon, Gábor, 2016. "GEFCom2014: Probabilistic solar and wind power forecasting using a generalized additive tree ensemble approach," International Journal of Forecasting, Elsevier, vol. 32(3), pages 1087-1093.
    2. Zhang, Yao & Wang, Jianxue, 2016. "K-nearest neighbors and a kernel density estimator for GEFCom2014 probabilistic wind power forecasting," International Journal of Forecasting, Elsevier, vol. 32(3), pages 1074-1080.
    3. Huang, Jing & Perry, Matthew, 2016. "A semi-empirical approach using gradient boosting and k-nearest neighbors regression for GEFCom2014 probabilistic solar power forecasting," International Journal of Forecasting, Elsevier, vol. 32(3), pages 1081-1086.
    4. Antonio Bracale & Guido Carpinelli & Pasquale De Falco, 2019. "Developing and Comparing Different Strategies for Combining Probabilistic Photovoltaic Power Forecasts in an Ensemble Method," Energies, MDPI, vol. 12(6), pages 1-16, March.
    5. Ma, Zhengjing & Mei, Gang, 2022. "A hybrid attention-based deep learning approach for wind power prediction," Applied Energy, Elsevier, vol. 323(C).
    6. Gergo Barta & Benedek Pasztor & Venkat Prava, 2021. "Optimized Charge Controller Schedule in Hybrid Solar-Battery Farms for Peak Load Reduction," Energies, MDPI, vol. 14(22), pages 1-18, November.
    7. Lobo, Benjamin J. & Brown, Donald E. & Grazaitis, Peter J., 2019. "Long-term forecasting of fuel demand at theater entry points," International Journal of Forecasting, Elsevier, vol. 35(2), pages 502-520.
    8. Halužan, Marko & Verbič, Miroslav & Zorić, Jelena, 2020. "Performance of alternative electricity price forecasting methods: Findings from the Greek and Hungarian power exchanges," Applied Energy, Elsevier, vol. 277(C).
    9. Mingzhe Zou & Sasa Z. Djokic, 2020. "A Review of Approaches for the Detection and Treatment of Outliers in Processing Wind Turbine and Wind Farm Measurements," Energies, MDPI, vol. 13(16), pages 1-30, August.
    10. Casado Yusta, Silvia & Nœ–ez Letamendía, Laura & Pacheco Bonrostro, Joaqu’n Antonio, 2018. "Predicting Corporate Failure: The GRASP-LOGIT Model || Predicci—n de la quiebra empresarial: el modelo GRASP-LOGIT," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 26(1), pages 294-314, Diciembre.
    11. Ren, Yuting & Li, Zhuolin & Xu, Lingyu & Yu, Jie, 2023. "The data-based adaptive graph learning network for analysis and prediction of offshore wind speed," Energy, Elsevier, vol. 267(C).
    12. 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.
    13. Wang, Hao & Ye, Jingzhen & Huang, Linxuan & Wang, Qiang & Zhang, Haohua, 2023. "A multivariable hybrid prediction model of offshore wind power based on multi-stage optimization and reconstruction prediction," Energy, Elsevier, vol. 262(PA).
    14. Mangalova, Ekaterina & Shesterneva, Olesya, 2016. "K-nearest neighbors for GEFCom2014 probabilistic wind power forecasting," International Journal of Forecasting, Elsevier, vol. 32(3), pages 1067-1073.

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