Cross-scale fusion network and two-stage decomposition for power forecasting of offshore wind turbines
Küçük Resim Yok
Tarih
2025
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Pergamon-Elsevier Science Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
This research presents a hybrid forecasting model for offshore wind power, which is based on a two-stage decomposition process and a densely connected convolutional network. Initially, the offshore wind power data is decomposed into several components utilizing an improved complete ensemble empirical mode decomposition with adaptive noise. The high-frequency component is further divided into multiple components through empirical mode decomposition. Following the decomposition, the dataset is transformed into the HSV color space. The proposed model features a sequential and multi-scale convolutional block architecture, inspired by the clique network approach. Furthermore, a squeeze-and-excitation module is incorporated to enhance the network performance. Comparative experiments are conducted against state-of-the-art deep learning models using data from two offshore wind turbines. The results indicate that the proposed model achieves superior performance metrics for WT3 and WT4, with root mean square error, mean absolute error, and mean absolute percentage error values ranging from 4.4796 to 4.8578, 3.2736 to 3.6543, and 0.2127 to 0.2193 for 1-h ahead forecast; 4.9674 to 5.7693, 3.5980 to 4.2028, and 0.2214 to 0.2295 for 3-h ahead forecast; and 5.8889 to 5.6338, 4.4247 to 4.1148, and 0.3064 to 0.2436 for 5-h ahead forecast, respectively. This pioneering two-stage decomposition and cross-scale CNN outperforms benchmarks by up to 74 % in RMSE. The proposed methodology improves short-term offshore wind power prediction by removing irregularities in the datasets.
Açıklama
Anahtar Kelimeler
Offshore Wind Power Forecasting, Multi-Stage Decomposition, Dense Network, Clique Network, Squeeze And Excitation Module
Kaynak
Ocean Engineering
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
341
