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Nonparametric wind power forecasting under fixed and random censoring

Author

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  • Dahl, Christian M.
  • Effraimidis, Georgios
  • Pedersen, Mikkel H.

Abstract

We consider nonparametric forecasting of wind power for individual wind turbines, allowing for random right censoring as well as two-sided fixed censoring. We propose a very fast estimation algorithm and show that this estimator of the unknown regression function is uniformly consistent. We argue that the key statistical features of the proposed nonparametric regression framework, such as nonlinearities and fixed and random censoring, are all needed in order to properly capture the main characteristics of wind power production functions. We show by a simulation study that the asymptotic properties of the estimator also holds in finite samples. We provide an empirical illustration comparing the forecasting accuracy of the proposed nonparametric regression model to some of the existing and popular forecasting devices used for predicting short to medium term wind power production at the individual turbine level. The empirical results are generally very encouraging.

Suggested Citation

  • Dahl, Christian M. & Effraimidis, Georgios & Pedersen, Mikkel H., 2019. "Nonparametric wind power forecasting under fixed and random censoring," Energy Economics, Elsevier, vol. 84(C).
    • Handle: RePEc:eee:eneeco:v:84:y:2019:i:c:s0140988319303093
    DOI: 10.1016/j.eneco.2019.104520
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    References listed on IDEAS

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    1. Chao Chen & Jamie Twycross & Jonathan M Garibaldi, 2017. "A new accuracy measure based on bounded relative error for time series forecasting," PLOS ONE, Public Library of Science, vol. 12(3), pages 1-23, March.
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    5. Jooyoung Jeon & James W. Taylor, 2012. "Using Conditional Kernel Density Estimation for Wind Power Density Forecasting," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(497), pages 66-79, March.
    6. Lee, Sokbae & Lewbel, Arthur, 2013. "Nonparametric Identification Of Accelerated Failure Time Competing Risks Models," Econometric Theory, Cambridge University Press, vol. 29(5), pages 905-919, October.
    7. Qi Li & Jeffrey Scott Racine, 2006. "Density Estimation, from Nonparametric Econometrics: Theory and Practice," Introductory Chapters, in: Nonparametric Econometrics: Theory and Practice, Princeton University Press.
    8. Sanchez, Ismael, 2006. "Short-term prediction of wind energy production," International Journal of Forecasting, Elsevier, vol. 22(1), pages 43-56.
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    Cited by:

    1. Pliego Marugán, Alberto & García Márquez, Fausto Pedro & Pinar Pérez, Jesús María, 2022. "A techno-economic model for avoiding conflicts of interest between owners of offshore wind farms and maintenance suppliers," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    2. Alberto Pliego Marug'an & Fausto Pedro Garc'ia M'arquez & Jes'us Mar'ia Pinar P'erez, 2024. "A techno-economic model for avoiding conflicts of interest between owners of offshore wind farms and maintenance suppliers," Papers 2401.08251, arXiv.org.

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    More about this item

    Keywords

    Kaplan-Meier estimator; Nonparametric methods; Time series;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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