Tariq Ali1,2,*, Muhammad Ayaz1,2, Mohammad Hijji2, Imran Baig3, MI Mohamed Ershath4, Saleh Albelwi2
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3839-3858, 2025, DOI:10.32604/cmes.2025.073169
- 23 December 2025
Abstract The integration of renewable energy sources into electricity markets presents significant challenges due to the inherent variability and uncertainty of power generation from wind, solar, and other renewables. Accurate forecasting is crucial for ensuring grid stability, optimizing market operations, and minimizing economic risks. This paper introduces a hybrid forecasting framework incorporating fractional-order statistical models, fractal-based feature engineering, and deep learning architectures to improve renewable energy forecasting accuracy. Fractional autoregressive integrated moving average (FARIMA) and fractional exponential smoothing (FETS) models are explored for capturing long-memory dependencies in energy time-series data. Additionally, multifractal detrended fluctuation analysis (MFDFA) More >