| Abstract: |
Accurate wind speed forecasting remains one of the most intractable challenges in renewable energy integration and atmospheric science. This paper introduces WuWeiDL, a novel hybrid deep learning architecture that synthesizes classical Chinese Daoist philosophy—specifically the concept of wu wei (non-forcing action) and feng-qi (wind-energy dynamics)—into a principled algorithmic framework for wind speed prediction at multiple horizons (1 h to 48 h). Inspired by Liezi's mythological "riding the wind" and the philosophy of effortless alignment with natural flow, WuWeiDL incorporates a Wu Wei Gating (WWG) module that adaptively suppresses over-parameterised attention in turbulent regimes, a Feng-Qi Turbulence Encoder (FQTE) that maps atmospheric boundary layer dynamics into latent embeddings, a bidirectional LSTM (BiLSTM) for capturing long-range temporal dependencies, and a transformer-based self-attention decoder. Evaluated on five benchmark wind farm datasets (inner Mongolia steppe, Danish North Sea, Texas Panhandle, Arabian Peninsula, and Tibetan Plateau), WuWeiDL achieves root-mean-square errors (RMSE) of 0.312–0.921 m/s across forecast horizons, improving upon state-of-the-art baselines by 20.4–46.8%. Ablation studies confirm synergistic contributions from all four components, with the Wu Wei Gate alone contributing a 19.8% RMSE reduction in high-turbulence conditions. The framework is openly interpretable via attention visualisations and offers a fresh epistemological bridge between Eastern philosophy of natural harmony and Western data-driven modelling, suggesting that "yielding to the wind's nature" rather than forcing predictions may be the key to ultra-accurate wind forecasting. |
