CEEMDAN–SSA–RWKV–SMA: A Robust Hybrid Model for Long-Term Wind Speed Forecasting in India

Reliable long-term wind speed forecasting is a critical requirement for the strategic deployment and operational stability of wind energy systems, particularly in meteorologically diverse regions like India. This study proposes a novel hybrid framework, CEEMDAN–SSA–RWKV–SMA, which integrates advanced signal decomposition, deep sequence modeling, and metaheuristic optimization. Initially, the raw wind speed time series is decomposed using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) to extract multi-scale Intrinsic Mode Functions (IMFs). To enhance signal clarity and reduce dimensionality, each IMF is further processed using Singular Spectrum Analysis (SSA). The resulting denoised and trend-extracted components are modeled using the Receptance Weighted Key Value (RWKV) neural network, a recent Transformer-RNN hybrid designed to capture long-range temporal dependencies efficiently. To optimize RWKV hyperparameters and SSA windowing parameters, the Slime Mould Algorithm (SMA) is employed as a global metaheuristic optimizer. Empirical evaluations on multi-regional Indian wind datasets demonstrate that the proposed framework consistently outperforms conventional models such as LSTM, Transformer, and CEEMDAN-LSTM in terms of MAE, RMSE, and MAPE. The proposed CEEMDAN-SSA-RWKV-SMA framework is a reliable forecasting strategy for improving wind energy integration in non-stationary and resource-critical environments.

@article{Vidya2026,
  title     = {CEEMDAN–SSA–RWKV–SMA: A Robust Hybrid Model for Long-Term Wind Speed Forecasting in India},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {3},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {S. Vidya},
  doi       = {10.14569/IJACSA.2026.0170335},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170335}
}