水利水电技术（中英文）

为提升超短期风电功率预测的准确性，提出了一种融合气象预报与卷积简化长短期记忆网络(ConvSLSTM)的高效预测模型。首先，针对风电数据中常见的异常值问题，采用箱线图法进行异常值检测，并结合K近邻互补法对检测出的异常值进行修正，以提升数据质量与可信度。其次，为简化模型输入并增强特征相关性，利用最大信息系数(MIC)筛选出与风电功率高度相关的气象因子，如风速与轮毂高度风速。在此基础上，结合长短期记忆网络(LSTM)引入卷积运算，强化特征提取能力，并设计交叉耦合的新型门控结构与窥视孔机制，以减少网络参数，提高模型训练效率。最后，结合历史数据与数值气象预报数据，基于ConvSLSTM模型对风电功率序列进行高精度预测。试验结果表明，所提模型在多个预测步长下，相较于无预报的ConvSLSTM、预报-LSTM与预报-Transformer等模型，其平均绝对误差(MAE)分别降低了42.18%、2.26%与32.66%。基于气象预报的ConvSLSTM模型展现出更高的预测精度、更强的鲁棒性与更优的泛化能力，充分验证了其在风电功率预测任务中的显著优势，具备良好的应用前景。

To enhance the accuracy of ultra-short-term wind power forecasting, this study proposes an efficient prediction model that integrates meteorological forecasts with a Convolutional Simplified Long Short-Term Memory Network(ConvSLSTM).Firstly, to address the common issue of outliers in wind power datasets, the boxplot method is applied for outlier detection, and the K-nearest neighbor imputation method is utilized to correct the detected outliers, thereby improving data quality and reliability. Secondly, to simplify model inputs and enhance feature relevance, the Maximal Information Coefficient(MIC) is employed to identify key meteorological factors strongly correlated with wind power, such as wind speed and hub-height wind speed. Subsequently, convolutional operations are incorporated into the Long Short-Term Memory(LSTM) network to strengthen feature extraction capabilities. A novel cross-coupled gating mechanism and peephole structure are further designed to reduce network parameters and improve training efficiency. Finally, leveraging both historical data and numerical weather prediction data, the ConvSLSTM model is used to achieve high-precision forecasting of wind power sequences.Experimental result demonstrate that, compared with ConvSLSTM without forecasts, Forecast-LSTM, and Forecast-Transformer models, the proposed method achieves reductions in mean absolute error(MAE) of 42.18%, 2.26%, and 32.66%, respectively, across multiple forecasting horizons.The ConvSLSTM model based on meteorological forecasts exhibits superior prediction accuracy, stronger robustness, and better generalization capability. These result validate its significant advantages in wind power forecasting tasks and indicate its broad application potential.