Forecasting electricity production from various energy sources in Türkiye: A predictive analysis of time series, deep learning, and hybrid models
Energy, cilt.286, 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
- Anahtar Kelimeler: Deep learning models, Electricity production, Forecasting, Hybrid models, Renewable energy sources, Time series analysis
- Süleyman Demirel Üniversitesi Adresli: Hayır
Özet
When it comes to energy sources used in electricity production, the future forecasting of electricity production from renewable energy sources is highly important for both the success of technological advancements in the renewable energy field and energy security. To forecast electricity production from renewable energy sources reliably, it is necessary to accurately model the components of the relevant series. The central argument of this paper is that the various components derived from electricity production data, particularly the residual component, retain valuable predictive information despite their intricate and nonlinear nature. While linear modelling may be highly accurate initially, repeating residuals within linear structures is a discrepancy in terms of data type and methodology. In this paper, different types of hybrid models that combine a decomposition method and both machine learning and statistical approaches are suggested for forecasting electricity production from different energy sources.