An improved residual-based convolutional neural network for very short-term wind power forecasting
Yükleniyor...
Dosyalar
Tarih
2021
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
ELSEVIER
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
An accurate forecast of wind power is very important in terms of economic dispatch and the operation of power systems. However, effectively mitigating the risks arising from wind power in power system operations greatly reduces the risk of wind energy producers, exposing them to potential additional costs. Being aware of this challenge, we introduced a two-step novel deep learning method for wind power forecasting. The first stage includes processes of Variational Mode Decomposition (VMD)-based feature extraction and converting these features into images. In the second stage, an improved residual-based deep Convolutional Neural Network (CNN) was utilized to forecast wind power. Meteorological wind speed, wind direction, and wind power data, which are directly related to each other, were employed as a dataset. The combined dataset was procured from a wind farm in Turkey between January 1 and December 31, 2018. The results of the proposed method were compared with the results obtained from the state-of-the-art deep learning architectures namely SqueezeNet, GoogLeNet, ResNet-18, AlexNet, and VGG-16 as well as physical model based on available meteorological forecast data. The proposed method outperformed the other architectures and demonstrated promising results for very short-term wind power forecasting due to its competitive performance.
Açıklama
Anahtar Kelimeler
Wind power forecasting, Residual network, Convolutional neural network, Variational mode decomposition, Deep learning
Kaynak
Energy Conversion and Management
WoS Q Değeri
Q1
Scopus Q Değeri
Cilt
228
Sayı
113731
Künye
Yildiz, C., Acikgoz, H., Korkmaz, D., & Budak, U. (2021). An improved residual-based convolutional neural network for very short-term wind power forecasting. Energy Conversion and Management, 228, 113731.
