Deep Learning-Based Prediction of Wind Power for Multi-turbines in a Wind Farm - Chen, Xiaojiao; Zhang, Xiuqing; Dong, Mi; Huang, Liansheng; Guo, Yan; He, Shiying (Frontiers in Energy Research, 2021)
The prediction of wind power plays an indispensable role in maintaining the stability of the entire power grid. In this paper, a deep learning approach is proposed for the power prediction of multiple wind turbines. Proceeding from the wind power time series, a two-stage modeling strategy is present, in which a deep neural network that combines spatiotemporal correlation to simultaneously predict the power of multiple wind turbines. Specifically, the network is a joint model composed of Long Short-Term Memory Network (LSTM) and Convolutional Neural Network (CNN). Herein, the LSTM captures the temporal dependence of the historical power sequence, while the CNN extracts the spatial features among the data, thereby achieving the power prediction for multiple wind turbines. Taking the measured data from an offshore wind farm in China, the prediction results of the model are compared with the true values in terms of error indicators. Compared with other methods, the prediction results by the proposed method are more precise.