Offshore Wind Power Prediction Based on Improved Long-term Recurrent Convolutional Neural Network

Accurate wind power prediction is of great significance to the safe connection of offshore wind power for the grid. Different from the land, the sea has the characteristics of complicated meteorological factors and significant fluctuation of wind power output, which makes the prediction accuracy of offshore wind power difficult to meet the practical engineering requirements. Aiming at the above problems, this paper proposes a prediction model based on the improved long-term recurrent convolutional neural network (LRCN) for ultra-short-term offshore wind power prediction. Firstly, the improved LRCN is used for preliminary power prediction, that is, the multi-convolution channel is constructed to extract the time series characteristics of variables at different layers, and the network convergence effect is improved by the forward-looking improved Adam optimizer. Secondly, the swing window algorithm and the clustering of fluctuation characteristics are used to classify the types of output fluctuation in the predicted period. Thirdly, error correction models are established for different fluctuation types, and the strongly correlated feature factors screened by the Xgboost algorithm are input to achieve error correction. Finally, experiments with data of actual offshore wind farm is put forward, and the results show that the proposed method can effectively predict the ultra-short-term offshore wind power, and the prediction accuracy is higher than that of traditional prediction models. © 2021 Automation of Electric Power Systems Press.