Research on Wind Power Mapping Relationship Based on Wind Speed Probability Modeling and Hybrid Neural Networks

To address the volatility of wind power output under high-proportion renewable energy grid integration, this paper proposes a wind speed probability modeling method and establishes a mapping relationship between wind speed and wind power output based on a hybrid neural network. By constructing a VAE-RealNVP hybrid model, we utilize flow-based transformations to optimize the latent space distribution, overcoming the limitations of traditional statistical models in characterizing multi-seasonal complex wind speed features, thereby achieving high-fidelity generation of high-dimensional temporal wind speed data. Subsequently, a CNN-GRU architecture is employed, coupling spatial feature extraction with temporal dependency modeling, to establish a nonlinear wind speed-to-power mapping relationship that incorporates dynamic wind curtailment constraints. Experimental results demonstrate that the proposed VAE-RealNVP model exhibits significant advantages in the quality, accuracy, and robustness of generated wind speed data, proving its effectiveness and potential in wind speed probability distribution modeling. Meanwhile, the CNN-GRU model outperforms in wind speed-to-power mapping, indicating its promising application potential in wind power forecasting.