Wind speed estimation using novelty hybrid adaptive estimation model based on decomposition and deep learning methods (ICEEMDAN-CNN)
Yazarlar (2)
Prof. Dr. Cem EMEKSİZ Tokat Gaziosmanpaşa Üniversitesi, Türkiye
Mustafa Tan Tokat Gaziosmanpaşa Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | Energy (Q1) | ||
| Dergi ISSN | 0360-5442 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | Türkçe | Basım Tarihi | 06-2022 |
| Cilt / Sayı / Sayfa | 249 / 1 / – | DOI | 10.1016/j.energy.2022.123785 |
| Makale Linki | http://dx.doi.org/10.1016/j.energy.2022.123785 | ||
| Özet |
| Estimating the wind speed correctly and reliably plays a key role in managing and operating wind energy power systems. Therefore an novelty adaptive estimation model (NAEM) combined with deep learning-based mode discretization has been developed for use in wind speed estimation in this study. This developed model consists of the improved complete e... |
| Anahtar Kelimeler |
| Adaptif estimation model | CNN | Hybrid deep learning | ICEEMDAN | Wind speed estimation |
Science Direct
Wind speed estimation using novelty hybrid adaptive estimation model based on decomposition and deep learning methods (ICEEMDAN-CNN)
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Scopus | 29 |
| Web of Science | 40 |