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Machine learning methods for GEFCom2017 probabilistic load forecasting
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DOI：10.1016/j.ijforecast.2019.02.002.png)
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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.
Keyword:
Global energy forecasting competition
Quantile random forest
Gradient boosting
Neural networks
Deep learning
Ensemble forecasting
Probabilistic forecasting
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