Branch error reduction criterion-based signal recursive decomposition and its application to wind power generation forecasting.
PLoS One
; 19(3): e0299955, 2024.
Article
de En
| MEDLINE
| ID: mdl-38517881
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.
Texte intégral:
1
Collection:
01-internacional
Base de données:
MEDLINE
Sujet principal:
Vent
Langue:
En
Journal:
PLoS One
Sujet du journal:
CIENCIA
/
MEDICINA
Année:
2024
Type de document:
Article
Pays d'affiliation:
Chine