基于近邻成分分析的短期风电功率集成预测

针对短期风电功率预测关键气象因素影响程度的差异和单一模型预测精度不足的问题，提出一种基于近邻成分分析(neighborhood components analysis, NCA)特征加权和Stacking集成预测的短期风电功率预测模型。考虑气象特征对风电功率影响程度不同，利用NCA对气象特征进行加权，将加权特征作为模型输入，强化关键特征的影响程度；在此基础上，构建多个基预测器预测风电功率，并利用结合器将预测结果融合，建立Stacking集成预测模型。算例分析表明，以加权特征作为输入的Stacking集成预测模型具有更高的短期风电功率预测精度。

Short-term Wind Power Ensemble Prediction Based on Neighborhood Components Analysis

In order to address the problems of different degrees of influence of key meteorological factors on short-term wind power prediction and the lack of prediction accuracy of a single mode, a short-term wind power prediction model based on neighborhood component analysis (NCA) feature weighting and Stacking ensemble prediction is proposed in this paper. Considering the different degrees of influence of meteorological features on wind power, NCA is employed to weight the meteorological features. The weighted features are used as model inputs to enhance the degree of influence of key features. On this basis, multiple base predictors are established to predict wind power, and a combiner is established to integrate the prediction results to build the Stacking ensemble learning prediction model. The analysis of examples shows that the higher accuracy for short-term wind power prediction is achieved with the Stacking ensemble forecasting model using weighted features as input.