Short-Term Prediction Method of Offshore Wind Power Based on Adaptive Error Correction

In the process of offshore wind power prediction, errors will inevitably occur. In order to improve the accuracy of offshore wind power prediction and the credibility of its prediction results, it is of great significance to deal with errors reasonably. In view of the above problems, this paper proposes a SOM-EEMD-GRU offshore wind power prediction method based on adaptive error correction. Firstly, SOM is used to cluster the original wind power data, and different types of weather are divided to form a preliminary wind power data set. Then, the Pearson correlation coefficient is used to screen the high correlation features in the preliminary data set, and the EEMD is used to decompose the power data, and the two are fused to form an amplified data set. Finally, the amplified offshore wind power data set is input into the GRU model based on residual connection to obtain the final prediction result. The experimental results show that compared with the comparison model, the proposed model has higher prediction accuracy in the offshore wind power prediction task.