一种风电功率的复合预测

Compound forecasting a wind power

本文以东南沿海地区某风力发电场数据为背景,在分析原始数据特点后,确定了相应的缺失数据的填补方法以及数据的预分解方法。之后针对数据预处理结果建立了基于时间序列和优化的BP神经网络复合预测模型,并给出风电功率预测结果。最后比较了复合模型与其它模型预测的均方误差以说明复合预测模型在提高预测精度方面的优势。

In this paper,a wind power generation field data in southeast coastal area as the background, in the analysis of the original data characteristics,determine the missing data filling method and corresponding data pre decomposition method.After data preprocessing results according to the established BP neural network composite time series prediction model and Optimization Based on wind power,and gives the result.Mean square error and finally compared the prediction of composite model and other models to illustrate the advantage of combined forecasting model in improving the accuracy of prediction.