Machine learning methods for GEFCom2017 probabilistic load forecasting

Slawek Smyl and N. Grace Hua

International Journal of Forecasting, 2019, vol. 35, issue 4, 1424-1431

Abstract: This paper describes the preprocessing and forecasting methods used by team Orbuculum during the qualifying match of the Global Energy Forecasting Competition 2017 (GEFCom2017). Tree-based algorithms (gradient boosting and quantile random forest) and neural networks made up an ensemble. The ensemble prediction quantiles were obtained by a simple averaging of the ensemble members’ prediction quantiles. The result shows a robust performance according to the pinball loss metric, with the ensemble model achieving third place in the qualifying match of the competition.

Keywords: Global energy forecasting competition; Quantile random forest; Gradient boosting; Neural networks; Deep learning; Ensemble forecasting; Probabilistic forecasting (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:35:y:2019:i:4:p:1424-1431

DOI: 10.1016/j.ijforecast.2019.02.002

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