AI 中文总结
针对日前电价预测中不同粒度预测不连贯及单一模型非全优的问题，开发时间相干组合方法，汇集多级别预测，经多种估计器比较，用四类模型基础预测验证，该组合预测在各时间级别表现出色。
AI 中文摘要
日前电价由许多竞争模型在多个时间粒度上进行预测，从每小时价格到时段和基荷产品。这些预测存在两个不同问题：不同粒度产生的预测不连贯，总量与各部分平均值不匹配；没有单一模型在每个市场、时期和水平上都是最准确的。我们开发了一种时间相干组合方法，一步解决这两个问题，将竞争专家在时间层次结构所有级别产生的预测汇集到一个满足聚合约束且在线性无偏组合中具有最小误差方差的单一预测中。由于最优解依赖于高维误差协方差矩阵，我们通过将相关结构与线性和非线性收缩方法交叉比较不同估计器。使用针对德国和西班牙日前市场的四类模型的基础预测，组合预测在几乎每个时间级别上都显著优于基础预测和最佳协调专家预测。
英文摘要
Day-ahead electricity prices are forecast by many competing models and at several temporal granularities, from hourly prices to block and baseload products. The resulting forecasts suffer from two distinct problems: forecasts produced at different granularities are incoherent, as the aggregates do not match the averages of their components, and no single model is the most accurate in every market, period and level. We develop a temporal coherent combination approach that addresses both problems in one step, pooling the forecasts that the competing experts produce at all the levels of a temporal hierarchy into a single forecast that satisfies the aggregation constraints and has minimum error variance among the linear unbiased combinations. Since the optimal solution depends on a high-dimensional error covariance matrix, we compare different estimators obtained by crossing correlation structures with linear and nonlinear shrinkage approaches. Using the base forecasts of four model classes for the German and Spanish day-ahead markets, the combined forecasts significantly outperform both the base forecasts and the best reconciled expert at nearly every temporal level.
