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K-nearest neighbors and a kernel density estimator for GEFCom2014 probabilistic wind power forecasting

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  • Zhang, Yao
  • Wang, Jianxue

Abstract

Probabilistic forecasts provide quantitative information in relation to energy uncertainty, which is essential for making better decisions on the operation of power systems with an increasing penetration of wind power. On the basis of the k-nearest neighbors algorithm and a kernel density estimator method, this paper presents a general framework for the probabilistic forecasting of renewable energy generation, especially for wind power generation. It is a direct and non-parametric approach. Firstly, the k-nearest neighbors algorithm is used to find the k closest historical examples with characteristics similar to the future weather condition of wind power generation. Secondly, a novel kernel density estimator based on a logarithmic transformation and a boundary kernel is used to construct wind power predictive density based on the k closest historical examples. The effectiveness of this approach has been confirmed on the real data provided for GEFCom2014. The evaluation results show that the proposed approach can provide good quality, reliable probabilistic wind power forecasts.

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  • 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.
    • Handle: RePEc:eee:intfor:v:32:y:2016:i:3:p:1074-1080
    DOI: 10.1016/j.ijforecast.2015.11.006
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    1. Zhang, Yao & Wang, Jianxue & Wang, Xifan, 2014. "Review on probabilistic forecasting of wind power generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 32(C), pages 255-270.
    2. 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.
    3. Hong, Tao & Pinson, Pierre & Fan, Shu, 2014. "Global Energy Forecasting Competition 2012," International Journal of Forecasting, Elsevier, vol. 30(2), pages 357-363.
    4. Tao Hong, 2014. "Energy Forecasting: Past, Present, and Future," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 32, pages 43-48, Winter.
    5. 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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    9. M. Mujahid Rafique & Shafiqur Rehman & Md. Mahbub Alam & Luai M. Alhems, 2018. "Feasibility of a 100 MW Installed Capacity Wind Farm for Different Climatic Conditions," Energies, MDPI, vol. 11(8), pages 1-18, August.
    10. 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.
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    12. Luis M. López-Manrique & E. V. Macias-Melo & O. May Tzuc & A. Bassam & K. M. Aguilar-Castro & I. Hernández-Pérez, 2018. "Assessment of Resource and Forecast Modeling of Wind Speed through An Evolutionary Programming Approach for the North of Tehuantepec Isthmus (Cuauhtemotzin, Mexico)," Energies, MDPI, vol. 11(11), pages 1-22, November.
    13. Li, Binghui & Feng, Cong & Siebenschuh, Carlo & Zhang, Rui & Spyrou, Evangelia & Krishnan, Venkat & Hobbs, Benjamin F. & Zhang, Jie, 2022. "Sizing ramping reserve using probabilistic solar forecasts: A data-driven method," Applied Energy, Elsevier, vol. 313(C).
    14. Abeer Alshejari & Vassilis S. Kodogiannis & Stavros Leonidis, 2020. "Development of Neurofuzzy Architectures for Electricity Price Forecasting," Energies, MDPI, vol. 13(5), pages 1-24, March.
    15. González-Sopeña, J.M. & Pakrashi, V. & Ghosh, B., 2021. "An overview of performance evaluation metrics for short-term statistical wind power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    16. Liu, Tingting & Xu, Jiuping, 2021. "Equilibrium strategy based policy shifts towards the integration of wind power in spot electricity markets: A perspective from China," Energy Policy, Elsevier, vol. 157(C).
    17. Yao Zhang & Wenxuan Yao & Shutang You & Wenpeng Yu & Ling Wu & Yi Cui & Yilu Liu, 2017. "Impacts of Power Grid Frequency Deviation on Time Error of Synchronous Electric Clock and Worldwide Power System Practices on Time Error Correction," Energies, MDPI, vol. 10(9), pages 1-15, August.
    18. Neeraj Bokde & Andrés Feijóo & Daniel Villanueva & Kishore Kulat, 2019. "A Review on Hybrid Empirical Mode Decomposition Models for Wind Speed and Wind Power Prediction," Energies, MDPI, vol. 12(2), pages 1-42, January.
    19. Qingtao Li & Jianxue Wang & Yao Zhang & Yue Fan & Guojun Bao & Xuebin Wang, 2020. "Multi-Period Generation Expansion Planning for Sustainable Power Systems to Maximize the Utilization of Renewable Energy Sources," Sustainability, MDPI, vol. 12(3), pages 1-18, February.
    20. Abdoos, Ali Akbar & Abdoos, Hatef & Kazemitabar, Javad & Mobashsher, Mohammad Mehdi & Khaloo, Hooman, 2023. "An intelligent hybrid method based on Monte Carlo simulation for short-term probabilistic wind power prediction," Energy, Elsevier, vol. 278(PA).
    21. Luo, Xing & Zhang, Dongxiao & Zhu, Xu, 2021. "Deep learning based forecasting of photovoltaic power generation by incorporating domain knowledge," Energy, Elsevier, vol. 225(C).

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