基于多属性决策和支持向量机的风电功率非线性组合预测

A Nonlinear Combined Model for Wind Power Forecasting Based on Multi-attribute Decision-making and Support Vector Machine

针对单一预测模型误差波动较大和线性组合预测的局限性,提出了基于多属性决策和支持向量机(SVM)的风电功率非线性组合预测模型。首先基于多属性决策理论,在检验其预测有效的情况下选择3种最优模型作为单项预测模型,并分别建模预测得到3种不同的预测结果;然后将各单项的预测结果作为训练输入,将相应的实际值作为训练输出,建立SVM组合预测模型。为检验该模型预测的有效性,用2组不同的历史数据进行验证,结果表明:该组合模型综合了各单项模型的优点,其均方根误差和平均百分比误差均小于各单项模型及其他组合模型,有效地提高了预测精度。最后还研究了采样间隔对预测结果的影响,结论表明:当采样间隔为5～15min时,预测精度较高。

Given the significant fluctuation of errors for single forecasting model and limitation of linear combined forecasting models,a nonlinear combined model for wind power forecasting based on multi-attribute decision-making and support vector machine（SVM） is proposed.Firstly,based on the theory of multi-attribute decision-making,the best three models are chosen as single forecasting models to verify the forecasting results.Three different results are obtained with each modeling and forecasting method separately.Then,SVM combined forecasting model is built by use of all single models as training inputs and corresponding actual values as training outputs.In order to validate the proposed model,two different historical data are used.The results show that the combined model effectively improves forecasting accuracy with synthesizing the advantages of all single forecasting models.Both root-mean-square error and mean percentage error are better than single models and other combined models.Finally,the effect of sampling interval on the forecasting results is studied,and the result indicates that the forecasting accuracy is more superior when sampling interval is between 5 minutes to 15 minutes.