Machine learning methods for GEFCom2017 probabilistic load forecasting

Smyl Slawek; Hua N. Grace

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Machine learning methods for GEFCom2017 probabilistic load forecasting

机译：GEFCom2017概率负载预测的机器学习方法

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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.

机译：本文介绍了Orbuculum团队在2017年全球能源预测竞赛（GEFCom2017）资格赛中使用的预处理和预测方法。基于树的算法（梯度增强和分位数随机森林）和神经网络构成一个整体。集合预测分位数是通过对集合成员的预测分位数进行简单平均得到的。结果显示出根据弹球损失指标的稳定表现，合奏模型在比赛资格赛中获得第三名。（C）2019由Elsevier B.V.代表国际预测协会发布。

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来源
《International journal of forecasting》 |2019年第4期|1424-1431|共8页
作者
Smyl Slawek; Hua N. Grace;
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作者单位

Uber Technol San Francisco CA 94103 USA;

Microsoft Redmond WA USA;

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原文格式 PDF
正文语种 eng
中图分类
关键词
Global energy forecasting competition; Quantile random forest; Gradient boosting; Neural networks; Deep learning; Ensemble forecasting; Probabilistic forecasting;

机译：全球能源预测竞赛;分位数的随机森林;梯度提升;神经网络;深度学习;整体预报;概率预测;

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机译：数据可视化和预测组合，用于GEFCom2017决赛中的概率负荷预测
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Machine learning methods for GEFCom2017 probabilistic load forecasting

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