The Effect of Data Decomposition on Prediction Performance in Wind Speed Prediction with Artificial Neural Network
Yazarlar (2)
Dr. Öğr. Üyesi Serkan ŞENKAL Tokat Gaziosmanpaşa Üniversitesi, Türkiye
Prof. Dr. Cem EMEKSİZ Tokat Gaziosmanpaşa Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (Diğer hakemli uluslarası dergilerde yayınlanan tam makale) | ||
| Dergi Adı | International Scientific and Vocational Studies Journal | ||
| Dergi ISSN | 2618-5938 | ||
| Dergi Tarandığı Indeksler | Google Scholar | ||
| Makale Dili | Türkçe | Basım Tarihi | 12-2023 |
| Cilt / Sayı / Sayfa | 7 / 2 / 213–223 | DOI | 10.47897/bilmes.1406384 |
| Makale Linki | http://dx.doi.org/10.47897/bilmes.1406384 | ||
| UAK Araştırma Alanları |
Yenilenebilir Enerji Sistemleri
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| Özet |
| This study investigates the effect of data decomposition to improve the performance of artificial neural networks (ANNs), widely used in wind speed forecasting in the wind energy sector. Artificial neural networks are essential tools for planning and optimizing the daily generation of wind power plants. However, prediction errors can lead to significant problems in power generation and energy grid management. The results show that data decomposition substantially affects the wind speed forecasting performance of neural networks. These findings are essential for researchers and industry professionals interested in developing more accurate forecasting models for power generation planning and management in the wind energy sector. By integrating artificial neural networks and data disaggregation methods, the study stands out as an essential step forward to improve the accuracy of wind speed forecasts and optimize the efficiency of wind energy facilities. |
| Anahtar Kelimeler |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Google Scholar | 4 |