为有效提高风电功率预测的精度，提出一种基于特征选择及误差修正的风电功率预测方法。综合分析风速、温/湿度、风向等特征对风电出力的影响，提出了正交化最大信息系数(orthogonalization maximal information coefficient，OMIC)结合预测模型的特征选择方法，可优选出适配于预测模型的特征维数。针对预测模型训练中会产生的固有误差，提出用动态模态分解(dynamic mode decomposition，DMD)来跟踪误差数据的时空模态，DMD最大的优点在于其数据驱动性质，不依赖于任何参数设定以及先验假设，可以实现更快捷、简便的误差预测。通过特征选择、误差修正来优化预测模型，以取得更精确的预测结果。基于北方某风电场单台风机实际数据，将所提方法与深度学习模型结合进行预测，并对比了相关预测指标，仿真结果表明本文所提方法能够有效提升预测精度。

To enhance the accuracy of wind power prediction, this paper presents a wind power prediction method that incorporates feature selection and error correction techniques. Initially, the impact of wind speed, temperature, humidity, wind direction and other features on wind power output is comprehensively analyzed. A feature selection approach based on orthogonalization maximal information coefficient (OMIC) combined with a prediction model is proposed to optimize the feature dimension of the model. In addition, to address the inherent errors that may arise during the prediction model training, a dynamic mode decomposition (DMD) approach is employed to track the spatiotemporal mode of error data. DMD is advantageous due to its data-driven nature, which eliminates dependence on any parameter settings and prior assumptions, thereby enabling faster and easier error prediction. By optimizing the prediction model through feature selection and error correction, the proposed method yields more accurate prediction results. To evaluate the effectiveness of the proposed method, actual data from a single wind turbine in a wind farm located in the north is employed. The proposed method is combined with a deep learning model, and relevant prediction indicators are compared. Simulation results demonstrate that the proposed method effectively improves prediction accuracy.