Machine learning methods for GEFCom2017 probabilistic load forecasting

doi:10.1016/j.ijforecast.2019.02.002

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Machine learning methods for GEFCom2017 probabilistic load forecasting

2019-10-01

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Slawek Smyl *

Ning Hua

DOI：10.1016/j.ijforecast.2019.02.002

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Abstract

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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. (C) 2019 Published by Elsevier B.V. on behalf of International Institute of Forecasters.

Keywords:

Global energy forecasting competition

Quantile random forest

Gradient boosting

Neural networks

Deep learning

Ensemble forecasting

Probabilistic forecasting

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