【摘要】 基于灰色关联和协整性理论,研究风电功率模型筛选与组合预测问题。分析6种单项预测方法与实际功率序列的灰关联度,并进行协整性检验,剔除冗余方法。算例分析结果表明,进行单项模型筛选能进一步提高组合预测模型的精度。然后,提出基于小世界优化的变权组合预测模型,并利用该模型对筛选后的方法进行组合预测。通过一个实例将小世界优化的变权组合预测模型与等权重平均组合预测法及协方差变权组合预测法进行仿真对比,该模型具有较高的预测精度和工作效率,可验证其在实际应用中的有效性和实用性。更多还原

【Abstract】 Based on the gray correlation and cointegration theory,the model selection and combination prediction ofwind power forecasting are studied. Six kinds of prediction methods were chosen to be analyzed the gray correlation withthe actual power sequence. In addition,cointegration test was adopted and the redundancy methods were removed. Thenumerical example results showed that the above model screening can further improve the accuracy of the model of thecombination prediction. Consequently,the variable weight combination prediction model based on small worldoptimization which is processed by the model screening is proposed to predict the wind power. Compared with thecombination prediction methods of equal weighted average and covariance variable weight based on the real data of windpower forecasting system,the proposed method is verified its effectiveness and practical utilities according to the desiredaccuracy and efficiency level.更多还原