Forecasting electricity production from various energy sources in Türkiye: A predictive analysis of time series, deep learning, and hybrid models

Gülay E., Şen M., Akgün Ö. B.

ENERGY, cilt.286, ss.1-14, 2024 (SCI-Expanded, Scopus)

• Yayın Türü: Makale / Tam Makale
• Cilt numarası: 286
• Basım Tarihi: 2024
• Doi Numarası: 10.1016/j.energy.2023.129566
• Dergi Adı: ENERGY
• Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Applied Science & Technology Source, Aquatic Science & Fisheries Abstracts (ASFA), CAB Abstracts, Communication Abstracts, Compendex, Computer & Applied Sciences, Environment Index, INSPEC, Metadex, Pollution Abstracts, Public Affairs Index, Veterinary Science Database, Civil Engineering Abstracts
• Sayfa Sayıları: ss.1-14
• Anahtar Kelimeler: Forecasting, Time series analysis, Deep learning models, Hybrid models, Electricity production, Renewable energy sources
• Dokuz Eylül Üniversitesi Adresli: Evet