论文标题
基于经验模式分解的负载和可再生时间序列的分析预测
Analysis of Empirical Mode Decomposition-based Load and Renewable Time Series Forecasting
论文作者
论文摘要
经验模式分解（EMD）方法及其变体已广泛用于负载和可再生预测文献中。使用这种多分辨率分解，与历史载荷和可再生生成有关的时间序列（TS）分解为几个固有模式函数（IMF），这些函数不那么非平稳和非线性。因此，理论上可以以更高的精度进行组件的预测。 EMD方法容易涉及多个问题，包括模态混叠和边界效应问题，但是基于TS分解的负载和可再生生成的预测文献主要集中于比较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预测模型在实时应用中的性能不佳。本文研究了这些问题及其在模型开发阶段的重要性。使用现实世界数据，提出了基于EMD的模型，并说明了边界效应的影响。
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.
