Title: Machine learning methods for GEFCom2017 probabilistic load forecasting
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.
Publication Year: 2019
Publication Date: 2019-05-07
Language: en
Type: article
Indexed In: ['crossref']
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Cited By Count: 27
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