High-precision wind speed soft measurement method for wind power prediction of wind power plant

The invention provides a high-precision wind speed soft measurement method for wind power prediction of a wind power plant, and provides a VAE-based feature learning network aiming at the characteristics of nonlinearity and strong dynamic property of NWP data, and adaptively endows each variable loss with different weights by using an entropy weight method, thereby improving a loss function of an original VAE model, and improving the prediction accuracy of the wind power of the wind power plant. Nonlinear feature extraction is carried out on a basic framework of a Gaussian mixture model, meanwhile, a regression device based on a BP neural network between a latent variable and the wind speed is constructed, finally, soft measurement of the wind speed is achieved, and high prediction precision is achieved. 本申请提供一种用于风电场风功率预测的高精度风速软测量方法，针对NWP数据具有非线性和强动态性的特点，提出了一种基于VAE的特征学习网络，同时利用熵权法给每个变量损失自适应地赋予不同的权重，从而改进原有VAE模型的损失函数，在高斯混合模型的基本框架上进行非线性的特征提取，同时构建潜隐变量与风速之间的基于BP神经网络的回归器，最终实现对风速的软测量，具备较高的预测精度。