The empirical mode decomposition (EMD) method and its variants have been extensively employed in the load and renewable forecasting literature. Using this multiresolution decomposition, time series (TS) related to the historical load and renewable generation are decomposed into several intrinsic mode functions (IMFs), which are less non-stationary and non-linear. As such, the prediction of the components can theoretically be carried out with notably higher precision. The EMD method is prone to several issues, including modal aliasing and boundary effect problems, but the TS decomposition-based load and renewable generation forecasting literature primarily focuses on comparing the performance of different decomposition approaches from the forecast accuracy standpoint; as a result, these problems have rarely been scrutinized. Underestimating these issues can lead to poor performance of the forecast model in real-time applications. This paper examines these issues and their importance in the model development stage. Using real-world data, EMD-based models are presented, and the impact of the boundary effect is illustrated.

中文翻译：

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基于经验模式分解的负荷分析和可再生时间序列预测

经验模式分解（EMD）方法及其变体已在负荷和可再生预测文献中得到广泛采用。使用这种多分辨率分解，将与历史负荷和可再生发电相关的时间序列（TS）分解为几个固有模式函数（IMF），这些固有函数不太平稳且非线性。这样，在理论上可以以明显更高的精度进行分量的预测。EMD方法容易出现模态混叠和边界效应等问题，但是基于TS分解的负荷和可再生能源发电的发电量预测文献主要集中在从预测精度的角度比较不同分解方法的性能。结果，这些问题很少受到审查。低估这些问题可能会导致实时模型中的预测模型性能不佳。本文研究了这些问题及其在模型开发阶段的重要性。使用实际数据，提出了基于EMD的模型，并说明了边界效应的影响。