为了解决风力发电和光伏发电随机性、波动性、间歇性造成的新能源功率预测建模和精度不高问题，基于深度学习模型变分自动编码器（Variational AutoEncoder,VAE）在时间序列建模和非线性逼近特征提取方面的优异性能，开展新能源电站VAE模型功率短期预测研究，并与循环神经网络（RNN）、长短期记忆(LSTM)深度学习方法和支持向量回归（SVR）机器学习方法的预测结果进行了对比。光伏电站和风电场独立功率预测结果表明，深度学习模型较基线机器学习模型预测性能更好，基于VAE的预测方法能够学习更高级别的特征，其预测性能表现更佳，光伏功率预测模型的RMSE、MAE和R2值分别为1.593、1.098和0.973；风光一体化功率预测结果表明，VAE和RNN模型能够提高功率预测准确性，其一体化功率预测模型的R2值分别为0.96和0.97

In order to solve the problem of new energy power prediction modeling and low precision caused by the randomness, volatility and intermittence of wind power generation and photovoltaic power generation, based on the excellent performance of variational autoencoder (VAE) deep learning model in the aspect of time series modeling and nonlinear approximation feature extraction, the VAE model was used to carry out the shortterm power prediction research of new energy power station. The prediction result of VAE was compared with the prediction results of the recurrent neural network (RNN) and long shortterm memory (LSTM) deep learning methods and the support vector regression (SVR) machine learning method. The independent power prediction results of photovoltaic power plants and wind farms show that the deep learning model has better prediction performance than the baseline machine learning model, and the VAEbased prediction method can learn higherlevel features and its prediction performance is better. The RMSE,MAE and R2 values of photovoltaic power prediction model are 1.593,1.098 and 0.973 respectively. The windsolar integrated power prediction results show that the VAE and RNN models can improve the accuracy of power prediction,and R2 values of integrated power prediction model are 0.96 and 0.91 respectively.

TM73

国家自然科学基金项目（71471070）