Machine learning methods for GEFCom2017 probabilistic load forecasting |
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Affiliation: | 1. Budapest University of Technology and Economics, Hungary;2. Ericsson, Hungary;3. Dmlab, Hungary;4. Enbritely, United Kingdom |
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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. |
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Keywords: | Global energy forecasting competition Quantile random forest Gradient boosting Neural networks Deep learning Ensemble forecasting Probabilistic forecasting |
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