A Systematic Evaluation of Current Architectures in Wind Power Forecasting

In three linesSystematic review of hybrid architectures for wind power interval forecasting. Approaches combining deep learning, modal decomposition (VMD, EEMD), and statistical methods improve accuracy. Dominant strategy: two independent models (LSTM, ELM) for lower/upper bounds. Challenges: lack of standardized metrics, computational complexity, limited real-world validation.

Summary generated by Claude — human-verified