Analysis of Empirical Mode Decomposition-based Load and Renewable Time Series Forecasting

11/23/2020 ∙ by Nima Safari, et al. ∙ 0 ∙ share

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.

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Analysis of Empirical Mode Decomposition-based Load and Renewable Time Series Forecasting

Authors

Dynamic mode decomposition for forecasting and analysis of power grid load data

Inspection of methods of empirical mode decomposition

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Analysis of Empirical Mode Decomposition-based Load and Renewable Time Series Forecasting

Authors

Dynamic mode decomposition for forecasting and analysis of power grid load data

Inspection of methods of empirical mode decomposition

An Introductory Study on Time Series Modeling and Forecasting

A New Unified Deep Learning Approach with Decomposition-Reconstruction-Ensemble Framework for Time Series Forecasting

Does Restraining End Effect Matter in EMD-Based Modeling Framework for Time Series Prediction? Some Experimental Evidences

A hemodynamic decomposition model for detecting cognitive load using functional near-infrared spectroscopy

A unified framework of epidemic spreading prediction by empirical mode decomposition based ensemble learning techniques

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