Probabilistic load forecasting via Quantile Regression Averaging of independent expert forecasts
Probabilistic load forecasting via Quantile Regression Averaging of independent expert forecasts
复制标题
通过独立专家预测的分位数回归平均进行概率负荷预测
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
R. Weron
中科院分区：
文献类型：
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作者：
Tao Hong;K. Maciejowska;J. Nowotarski;R. Weron
概率负荷预测在当今电力系统规划和运行中变得至关重要。我们提出了一种新的方法来计算电力需求的区间预测，该方法将分位数回归平均（QRA）技术应用于一组独立专家点预测。我们证明了所提出的方法的有效性，使用的数据从分层负荷预测轨道的全球能源预测竞赛2012年。结果表明，新方法能够提供更好的预测区间比四个基准模型的大部分负荷区和聚合水平。
Probabilistic load forecasting is becoming crucial in today's power systems planning and operations. We propose a novel methodology to compute interval forecasts of electricity demand, which applies a Quantile Regression Averaging (QRA) technique to a set of independent expert point forecasts. We demonstrate the effectiveness of the proposed methodology using data from the hierarchical load forecasting track of the Global Energy Forecasting Competition 2012. The results show that the new method is able to provide better prediction intervals than four benchmark models for the majority of the load zones and the aggregated level.