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Sequence of nonparametric models for GEFCom2014 probabilistic electric load forecasting

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  • Mangalova, Ekaterina
  • Shesterneva, Olesya

Abstract

The probabilistic forecasting method proposed in this paper is based on the use of the sequence of Nadaraya–Watson estimators. It allows estimates of quantiles to be obtained without assumptions as to the probability distribution. The effectiveness of the approach is demonstrated during the Global Energy Forecasting Competition 2014 in the probabilistic electric load forecasting track.

Suggested Citation

  • Mangalova, Ekaterina & Shesterneva, Olesya, 2016. "Sequence of nonparametric models for GEFCom2014 probabilistic electric load forecasting," International Journal of Forecasting, Elsevier, vol. 32(3), pages 1023-1028.
    • Handle: RePEc:eee:intfor:v:32:y:2016:i:3:p:1023-1028
    DOI: 10.1016/j.ijforecast.2015.11.001
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    References listed on IDEAS

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    1. 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.
    2. Härdle,Wolfgang, 1992. "Applied Nonparametric Regression," Cambridge Books, Cambridge University Press, number 9780521429504, October.
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    2. Paul Anton Verwiebe & Stephan Seim & Simon Burges & Lennart Schulz & Joachim Müller-Kirchenbauer, 2021. "Modeling Energy Demand—A Systematic Literature Review," Energies, MDPI, vol. 14(23), pages 1-58, November.
    3. van der Meer, D.W. & Widén, J. & Munkhammar, J., 2018. "Review on probabilistic forecasting of photovoltaic power production and electricity consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1484-1512.

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