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Data preprocessing and quantile regression for probabilistic load forecasting in the GEFCom2017 final match

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  • Kanda, Isao
  • Veguillas, J.M. Quintana

Abstract

Team QUINKAN competed in the GEFCom2017 final match of hierarchical probabilistic load forecasting by adopting the quantile regression method using the R package quantreg. The weather stations were clustered into 11 groups, from which an optimal one was chosen for each load meter using the boosting method. The load meter records were cleaned and/or supplemented by various methods in order to secure robust quantile predictions. The variation in the regression formulas was kept as small as possible by introducing measures for suppressing prediction instability, although special formulas were employed for loading meters that were of an industrial nature. Several procedures were applied to help improve the accuracy, such as the smoothing of season transitions, coarse graining of the relative humidity, the use of load-oriented day-type definition, the averaging of weather data, and outlier removal.

Suggested Citation

  • Kanda, Isao & Veguillas, J.M. Quintana, 2019. "Data preprocessing and quantile regression for probabilistic load forecasting in the GEFCom2017 final match," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1460-1468.
    • Handle: RePEc:eee:intfor:v:35:y:2019:i:4:p:1460-1468
    DOI: 10.1016/j.ijforecast.2019.02.005
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    References listed on IDEAS

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    1. Hong, Tao & Xie, Jingrui & Black, Jonathan, 2019. "Global energy forecasting competition 2017: Hierarchical probabilistic load forecasting," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1389-1399.
    2. Haben, Stephen & Giasemidis, Georgios, 2016. "A hybrid model of kernel density estimation and quantile regression for GEFCom2014 probabilistic load forecasting," International Journal of Forecasting, Elsevier, vol. 32(3), pages 1017-1022.
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    4. Dordonnat, V. & Pichavant, A. & Pierrot, A., 2016. "GEFCom2014 probabilistic electric load forecasting using time series and semi-parametric regression models," International Journal of Forecasting, Elsevier, vol. 32(3), pages 1005-1011.
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    Cited by:

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    6. VandenHeuvel, Daniel & Wu, Jinran & Wang, You-Gan, 2023. "Robust regression for electricity demand forecasting against cyberattacks," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1573-1592.

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