Branch error reduction criterion-based signal recursive decomposition and its application to wind power generation forecasting

Abstract

Due to the ability of sidestepping mode aliasing and endpoint effects, variational mode decomposition (VMD) is usually used as the forecasting module of a hybrid model in time-series forecasting. However, the forecast accuracy of the hybrid model is sensitive to the manually set mode number of VMD; neither underdecomposition (the mode number is too small) nor over-decomposition (the mode number is too large) improves forecasting accuracy. To address this issue, a branch error reduction (BER) criterion is proposed in this study that is based on which a mode number adaptive VMD-based recursive decomposition method is used. This decomposition method is combined with commonly used single forecasting models and applied to the wind power generation forecasting task. Experimental results validate the effectiveness of the proposed combination.

Kalman filtering to reduce measurement noise of sample entropy: An electroencephalographic study

“…VRR values for NNetEn entropy are higher than those for approximate entropy and sample entropy, indicating that NNetEn has lower measurement variance than approximate entropy for both sleep and epilepsy signals. We acknowledge that there exist a lot entropy measures (e.g., dispersion entropy [ 18 ], permutation entropy [ 51 , 52 ], fuzzy entropy [ 53 ]), and expect a future exploration of applicability to other entropy measures.…”