Temporal hierarchies with autocorrelation for load forecasting
Temporal hierarchies with autocorrelation for load forecasting
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DOI：
10.1016/j.ejor.2019.07.061
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发表时间：
2020-02
期刊：
影响因子：
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通讯作者：
P. Nystrup;Erik Lindström;P. Pinson;H. Madsen
中科院分区：
文献类型：
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作者：
P. Nystrup;Erik Lindström;P. Pinson;H. Madsen
我们提出了四种不同的估计器，它们在协调时间层次中的预测时考虑了自相关结构。结合来自多个时间聚合级别的预测可以利用信息差异并减轻模型的不确定性，而协调则可以确保统一的预测，从而支持不同视界的一致决策。在以往的研究中，预测的权重是由层次结构或预测误差方差来确定的，而没有考虑预测误差中潜在的自相关性。我们的第一个估计器考虑每个聚集水平内的自协方差矩阵。由于这可能难以估计，我们提出了混合自相关和方差信息的第二个估计器，但只需要估计每个聚集水平上的一阶自相关系数。我们的第三和第四估计器使用互相关矩阵及其逆的鲁棒估计促进聚合水平之间的信息共享。我们在模拟研究中比较了提出的估计器，并通过在瑞典四个价格区域的短期电力负荷预测应用证明了它们的实用性。我们发现，在协调预测时，通过考虑自动协方差和交叉协方差，可以显著提高所有频率和区域的准确性。
We propose four different estimators that take into account the autocorrelation structure when reconciling forecasts in a temporal hierarchy. Combining forecasts from multiple temporal aggregation levels exploits information differences and mitigates model uncertainty, while reconciliation ensures a unified prediction that supports aligned decisions at different horizons. In previous studies, weights assigned to the forecasts were given by the structure of the hierarchy or the forecast error variances without considering potential autocorrelation in the forecast errors. Our first estimator considers the autocovariance matrix within each aggregation level. Since this can be difficult to estimate, we propose a second estimator that blends autocorrelation and variance information, but only requires estimation of the first-order autocorrelation coefficient at each aggregation level. Our third and fourth estimators facilitate information sharing between aggregation levels using robust estimates of the cross-correlation matrix and its inverse. We compare the proposed estimators in a simulation study and demonstrate their usefulness through an application to short-term electricity load forecasting in four price areas in Sweden. We find that by taking account of auto- and cross-covariances when reconciling forecasts, accuracy can be significantly improved uniformly across all frequencies and areas.